Quantifying operating strategy energy usage

ABSTRACT

The present disclosure describes systems and methods for quantifying operating strategy energy usage. One embodiment describes an industrial control system that includes a tangible, non-transitory, computer readable medium storing a plurality of instructions executable by a processor of the industrial control system. The instructions include instructions to determine a plurality of operating strategies associated with an industrial automation component that is communicatively coupled to the industrial control system, in which each of the plurality of operating strategies includes a set of operational parameters associated with the industrial automation component, determine an expected energy usage cost associated with the industrial automation component for each of the plurality of operating strategies, determine an expected value added associated with the industrial automation component for each of the plurality of operating strategies, and select one of the plurality of operating strategies for operating the industrial automation component based at least in part on the expected energy usage cost and the expected value added associated with each of the plurality of operating strategies.

BACKGROUND

The present disclosure relates generally to energy usage, and moreparticularly, to determining and managing energy usage in a system orprocess.

Generally, a control system may be utilized to monitor and controlmachines or equipment in a process, such as a manufacturing process, ora system, such as an industrial automation system. For example, acontrol system may be included in a packaging factory to control thevarious machines in a beverage packaging process. Operating in theprocess or system, the machines and/or devices may use energy, whichpresents a cost for operation of the process or system. In other words,the energy usage by each individual machine or device, a group ofmachines or devices, and the process or system as a whole may be usefulfor controlling and/or monitoring operation of the system or process.

Accordingly, it would be beneficial to improve the determination ofenergy usage and the management of energy usage by the machines and/ordevices in a system or process.

BRIEF DESCRIPTION

Certain embodiments commensurate in scope with the originally claimedembodiments are summarized below. These embodiments are not intended tolimit the scope of the claimed invention, but rather these embodimentsare intended only to provide a brief summary of possible forms of thesystems and techniques described herein. Indeed, the systems andtechniques described herein may encompass a variety of forms that may besimilar to or different from the embodiments set forth below.

A first embodiment describes an industrial control system that includesa tangible, non-transitory, computer readable medium storing a pluralityof instructions executable by a processor of the industrial controlsystem. The instructions include instructions to determine a pluralityof operating strategies associated with an industrial automationcomponent that is communicatively coupled to the industrial controlsystem, in which each of the plurality of operating strategies includesa set of operational parameters associated with the industrialautomation component, determine an expected energy usage cost associatedwith the industrial automation component for each of the plurality ofoperating strategies, determine an expected value added associated withthe industrial automation component for each of the plurality ofoperating strategies, and select one of the plurality of operatingstrategies for operating the industrial automation component based atleast in part on the expected energy usage cost and the expected valueadded associated with each of the plurality of operating strategies.

Another embodiment describes a system that includes an industrialautomation component and an industrial control system that iscommunicatively couple to the industrial automation component. Theindustrial control system includes at least one processor thatdetermines a plurality of operating strategies associated with theindustrial automation component, in which each of the operatingstrategies includes a set of operational parameters associated with theindustrial automation component, determines an expected carbon footprintassociated with the industrial automation component for each of theplurality of operating strategies, determine an expected value addedassociated with the industrial automation component for each of theplurality of operating strategies, and selects one of the plurality ofoperating strategies for operating the industrial automation componentbased at least in part on the expected energy usage cost and theexpected value added associated with each of the plurality of operatingstrategies.

Another embodiment describes a method that includes quantifying, usingan industrial control system, an expected product throughput for each ofa plurality of operating strategies that may be implemented by anindustrial automation component, quantifying, using the industrialcontrol system, an expected product quality for each of the plurality ofoperating strategies, quantifying, using the industrial control system,an expected energy usage for each of the plurality of operatingstrategies, quantifying, using the industrial control system, anexpected up-time for each of the plurality of operating strategies,generating, using the industrial control system, a value-add index basedon the expected product throughput, the expected product quality, theexpected energy usage, the expected up-time, or any combination thereof,implementing, using the industrial control system, one of a plurality ofoperating strategies with the industrial automation component based onthe value-add index.

DRAWINGS

These and other features, aspects, and advantages of the presentdisclosure will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 illustrates a block diagram representing example hierarchicallevels of an industrial automation system, in accordance with anembodiment presented herein;

FIG. 2 illustrates a block diagram of an example control system that maybe employed within the industrial automation system of FIG. 1, inaccordance with an embodiment presented herein;

FIG. 3 illustrates a block diagram of components within the industrialcontrol system of the industrial automation system of FIG. 1, inaccordance with an embodiment presented herein;

FIG. 4 illustrates an example of the industrial automation system ofFIG. 1, in accordance with an embodiment presented herein;

FIG. 5 illustrates a flow diagram of a method for determining energyusage, in accordance with an embodiment presented herein;

FIG. 6 illustrates a block diagram of a group of components, inaccordance with an embodiment presented herein;

FIG. 7 illustrates a flow diagram of a method for determining energyusage of each component in the group of components of FIG. 6, inaccordance with an embodiment presented herein;

FIG. 8 illustrates a block diagram of a process with multiple stages, inaccordance with an embodiment presented herein;

FIG. 9 illustrates a flow diagram of a method for determining energyusage of each stage in the process of FIG. 8, in accordance with anembodiment presented herein;

FIG. 10 illustrates a flow diagram of a method for determining actualenergy used by a component or a group of components, in accordance withan embodiment presented herein;

FIG. 11 illustrates a block diagram of a product in a productionprocess, in accordance with an embodiment presented herein;

FIG. 12 illustrates a flow diagram of a method for tracking energy usagein the production process of FIG. 10, in accordance with an embodimentpresented herein;

FIG. 13 illustrates a flow diagram of a method for setting an energyusage baseline for a component or a group of components, in accordancewith an embodiment presented herein;

FIG. 14A illustrates a flow diagram of a method for detecting a fault ina component, in accordance with an embodiment presented herein;

FIG. 14B illustrates a flow diagram of a method for detecting a fault ina group of components, in accordance with an embodiment presentedherein;

FIG. 15 illustrates a flow diagram of a method for determining changesin operation of a component or a group of components, in accordance withan embodiment presented herein;

FIG. 16 illustrates a block diagram of an energy usage map for theindustrial automation system of FIG. 4, in accordance with an embodimentpresented herein;

FIG. 17 illustrates a flow diagram of a method for selecting andexecuting an operating plan based at least in part on expected energycost, in accordance with an embodiment presented herein;

FIG. 18 illustrates a flow diagram of a method for selecting andexecuting an operating plan based at least in part on expected carboncost, in accordance with an embodiment presented herein;

FIG. 19 illustrates a block diagram of an economic value-add index(EVI), in accordance with an embodiment presented herein; and

FIG. 20 illustrates a flow diagram of a method for generating aneconomic value-add index (EVI), in accordance with an embodimentpresented herein.

DETAILED DESCRIPTION

One or more specific embodiments of the present disclosure will bedescribed below. In an effort to provide a concise description of theseembodiments, all features of an actual implementation may not bedescribed in the specification. It should be appreciated that in thedevelopment of any such actual implementation, as in any engineering ordesign project, numerous implementation-specific decisions must be madeto achieve the developers' specific goals, such as compliance withsystem-related and business-related constraints, which may vary from oneimplementation to another. Moreover, it should be appreciated that sucha development effort might be complex and time consuming, but wouldnevertheless be a routine undertaking of design, fabrication, andmanufacture for those of ordinary skill having the benefit of thisdisclosure.

When introducing elements of various embodiments of the presentdisclosure, the articles “a,” “an,” “the,” and “said” are intended tomean that there are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.

As described above, control systems generally control and monitoroperation of machines and/or devices included in a system or process. Tosimplify the following discussion, the machines and/or devices aregenerally referred to herein as “components.” Accordingly, components ina system or process may include controllers, input/output (I/O) modules,motor control centers, motors, human machine interfaces (HMIs), operatorinterfaces, contactors, starters, sensors, drives, relays, protectiondevices, switchgear, compressors, scanners, gauges, valves, flow meters,and the like.

In operation, the components may use energy (e.g., electrical energy).For example, to actuate an electric motor (e.g., a component),electrical energy is supplied to the motor. Since there is generally acost for procuring energy, the energy usage by the various componentsmay be an operating cost of the system or process. More specifically,the cost may include the financial amount paid to a utility providerand/or the carbon credits used to generate the energy. Thus, to quantifythe associated costs of the system or process, the energy usage by eachcomponent may be determined. In some embodiments, the energy usage of acomponent may be determined by using a meter (e.g., power, voltage, orcurrent meter) to measure the power supplied to the component over time.However, placing meters in a system or process may in itself introduceadded cost to the operation of the system or process.

Accordingly, one embodiment of the present disclosure describes a methodfor determining the energy usage of components without directlymeasuring the energy usage of each component. In other words, as will bedescribed in more detail below, the energy usage of some components maybe inferred from the energy usage of other components in the process orsystem. For example, the energy usage of an electric motor driven by amotor drive may be determined by measuring the power input to the motordrive and subtracting the power used by the internal electronics of themotor drive. In some embodiments, the power used by the internalelectronics of the motor drive may be determined with a model based onprinciples of physics, specification information from the motormanufacturer, and/or previous operation of the motor drive.Additionally, the techniques described herein may enable the energyusage in a process or system to be determined at various levels ofgranularity, for example, at a component level, a cell level, an arealevel, a factory level, or a process level.

Based on the energy usage in a system or process, energy usage baselinesmay be determined. As used herein, an energy usage baseline describes anexpected energy usage of a component or a group of components at aparticular operational state. For example, based on past energy usagedata, it may be determined that a motor drive is expected to use400+/−55 kWh during the current operational state. Thus, as will bedescribed in more detail below, the energy usage baseline may enablediagnostics and/or prognostics on one or more components in the systemor process. For example, one embodiment of the present disclosuredescribes a method for detecting a potential fault in a component whenthe energy usage of the component exceeds a set energy usage baseline.In some embodiments, exceeding the energy usage baseline may trigger analarm or event to notify an operator of the potential fault.

Additionally, since the expected energy usage of the components in asystem or process may be determined by a control system, the affects ofadjusting operation of the components may be better quantified by thecontrol system. In other words, as will be described in more detailbelow, various operating plans for the system or process may beevaluated taking into account energy usage costs. More specifically, insome embodiments, an operating strategy (e.g., plan) may be selectedbased in part on the expected energy usage cost, the value added to aproduct, and any additional costs associated with the operation plan,such as energy usage allotments (e.g., caps), energy usage premiums, andmaintenance costs.

As described above, the techniques described herein may be utilized witha system or a process. Accordingly, by way of introduction, FIG. 1depicts a block diagram of an industrial automation system 10, which maybe any system in the material handling, packaging industries,manufacturing, processing, batch processing, or any technical field thatemploys the use of one or more industrial automation components. Morespecifically, the depicted industrial automation system 10 may bedivided into various hierarchical levels, such as factories 12, areas16, cells 18, and components 20. In one embodiment, the industrialautomation system 10 may include a factory 12 that may encompass part ofthe industrial automation system 10. As such, the industrial automationsystem 10 may include additional factories 14 that may be employed withthe factory 12 to perform an industrial automation process or the like.

Each factory 12 or 14 may be divided into a number of areas 16, forexample, based on the production processes performed. For example, afirst area 16 may include a sub-assembly production process and a secondarea 16 may include a core production process. In another example, eacharea 16 may be related to different operations performed in theindustrial automation system 10. For instance, in a packaging system, afirst area 16 may include a preparation process and a second area 16 mayinclude a packing process. Additionally or alternatively, the areas 16may be determined based on the physical location of components 20 in theindustrial automation system 10 or discipline areas of the industrialautomation system 10. For example, the areas may be divided into batchoperation areas, continuous operation areas, discrete operation areas,inventory operation areas, and the like.

The areas 16 may further be subdivided into cells 18, which are made upof individual components 20. More specifically, the cells 18 may includea particular group of industrial automation components 20 that performone aspect of a production process. For example, in the preparationprocess, a first cell 18 may include the components 20 used for loading,a second cell 18 may include the components 20 used for washing, and athird cell 18 may include the components 20 used for sealing.Additionally, in the packing process, a fourth cell 18 may includecomponents 20 used for sterilization, a fifth cell 18 may includecomponents 20 used for labeling, and a sixth cell 18 may includecomponents used for packing.

To facilitate carrying out the production processes, as described above,the components 20 may include controllers, input/output (I/O) modules,motor control centers, motors, human machine interfaces (HMIs), operatorinterfaces, contactors, starters, sensors, drives, relays, protectiondevices, switchgear, compressors, scanners, gauges, valves, flow meters,and the like. Accordingly, the components 20 may function to performvarious operations in the industrial automation system 10.

Additionally, as described above, the industrial control system 22 maymonitor and/or control operation of the components 20. As such, theindustrial control system 22 may be a computing device that includescommunication abilities, processing abilities, and the like. Forexample, the industrial control system 22 may include one or morecontrollers, such as a programmable logic controller (PLC), aprogrammable automation controller (PAC), or any other controller thatmay monitor or control an industrial automation component 20. Thus, theindustrial control system 22 may be communicatively coupled to variouscomponents 20, as depicted in FIG. 2. More specifically, as depicted,the industrial control system 22 is communicatively coupled to anotherindustrial control system 22A, an operator interface 24, a drive 26, amotor 27, and a conveyer 28 (e.g., components 20) via a communicationnetwork 29. In some embodiments, the communication network 29 may useEtherNet/IP, ControlNet, DeviceNet, or any other industrialcommunication network protocol.

More specifically, the industrial control system 22 may becommunicatively coupled to an operator interface 24, which may be usedto modify and/or view settings and operations of the industrial controlsystem 22. The operator interface 24 may be a user interface thatincludes a display and an input device, which may be used to communicatewith the industrial control system 22. In some embodiments, the operatorinterface 24 may be characterized as a human-machine interface (HMI), ahuman-interface machine, or the like included on a computing device thatinteracts with the control system 22, such as a a laptop, generalpurpose computer, a tablet, mobile device, and the like. In other words,as depicted, the operator interface 24 may include a display 25.Additionally, the industrial control system 22 may be communicativelycoupled to one or more other industrial control systems 22A. Morespecifically, the industrial control systems 22 and 22A may communicateinformation, such as reference points or other details regarding theindustrial automation system 10, to enable the industrial control system22 to become aware of the environment in which the industrial automationsystem 10 is operating.

The industrial control system 22 may also be communicatively coupled tocomponents 20 that perform specific operations in the industrialautomation system. For example, in the depicted embodiment, theindustrial control system 22 is coupled to the drive 26, which mayconvert an input alternating current (AC) voltage into a controllable ACvoltage using a rectifier circuit and an inverter circuit to drive amotor 27, which in turn may actuate the conveyer belt 28. Thus, theindustrial control system 22 may directly (e.g., drive 26) or indirectly(e.g., motor 27) control operation of the various components in theindustrial automation system 10.

With the forgoing in mind, the drive 26, the motor 27, and the conveyor28 may be considered to be a part of a particular cell 18, area 16,and/or factory 12. Accordingly, in addition to monitoring andcontrolling operation of individual components 20, the industrialcontrol system 22 may have the ability to monitor and control operationof the various cells 18, areas 16, and factories 14 in the industrialautomation system 10. For example, by adjusting the operation of thedrive 26 and indirectly the operation of the conveyer 28, the industrialcontrol system 22 may adjust the operation of a packaging process. Thus,as will be described in more detail below, by understanding how eachcomponent 20 may be related to the industrial automation system 10(e.g., with respect to each area 16, each cell 18, and each component20), the industrial control system 22 may manage operations (e.g.,production, energy usage, equipment lifecycle) of the industrialautomation system 10.

As discussed above, the industrial control system 22 may include acontroller or any computing device that includes communicationabilities, processing abilities, and the like. One embodiment of theindustrial control system 22 is described in FIG. 3. As depicted, theindustrial control system 22 includes a communication module 32, aprocessor 34, memory 36, a storage module 38, and input/output (I/O)ports 40. The processor 34 may be any type of computer processor ormicroprocessor capable of executing computer-executable instructions. Incertain embodiments, the processor 34 may include multiple processorsworking together.

The memory 36 and the storage module 38 may be any suitable article ofmanufacture that can serve as media to store processor-executable code,data, instructions, or the like. These articles of manufacture mayrepresent computer-readable media that may store theprocessor-executable code used by the processor 34 to perform thepresently disclosed techniques. The memory 36 and the storage 38 mayalso be used to store the data, analysis of the data, and the like. Thememory 36 and the storage 38 may represent non-transitorycomputer-readable media (i.e., any suitable form of memory or storage)that may store the processor-executable code used by the processor 34 toperform various techniques described herein. It should be noted thatnon-transitory merely indicates that the media is tangible and not asignal.

Additionally, the communication module 32 may facilitate communicationbetween the control system 22 and the industrial automation components20 and/or other industrial control systems 22. As described above, theindustrial control system 22 may monitor and control the operation ofeach respective component 20, cell 18, area 16, or factory 12.Accordingly, the control system 22 and the components 20 may communicatecontrol instructions, status information, and the like via thecommunication module 32.

Furthermore, to facilitate in the control and monitoring of thecomponents 20, the control system 22 may receive feedback informationrelating to operational parameters of the industrial control system. Aswill be described in more detail below, sensors may be placed in and/oraround the industrial automation system 10 to measure such operationalparameters. In some embodiments, the sensors may include pressuresensors, accelerometers, heat sensors, motion sensors, voltage sensors,and the like. For example, the control system 22 may determine theenergy usage of a particular component 20 based on the power measured bya power sensor over time. Accordingly, the operational parameters may bereceived by the control system 22 from the sensors via the I/O ports 40.

However, as described above, the addition of sensors may add to theoperating cost of the industrial automation system 10. Accordingly, aswill be described in more detail below, the operational parameters for afirst component 20 may enable the control system 22 to infer theoperational parameters of a second component 20 related to the firstcomponent 20. Thus, to facilitate inferring operational parameters ofthe second component 20, the control system 22 may determine therelationship between the first component and the second component. Inother words, the control system 22 may determine how the industrialautomation system 10 is subdivided, how each area 16, cell 18, andcomponent 20 interacts with one another, which components 20 are part ofeach factory 12, area 16, and cell 18, and the like. By understandingthe inter-relationships in the industrial automation system 10, thecontrol system 22 may determine how operational adjustments may directlyor indirectly affect the rest of the system. For example, the controlsystem 20 may adjust energy consumption of a first component 20 based onthe energy consumption of other components in the industrial automationsystem 10 to control overall energy usage by the industrial automationsystem 10. Additionally, as will be described in more detail below, thecontrol system 22 may determine an operational parameters for eachcomponent 20 based on criteria such as energy usage, total energyallotment, energy usage premiums, production mix, production levels, andthe like.

Keeping the foregoing in mind, an example of an industrial automationsystem 10 (e.g., a packaging factory 50) is described in FIG. 4. Morespecifically, the packaging factory 50 may be a high-speed packagingline used in the food and beverage industry to process beveragecontainers. As such, the packaging factory 50 may include industrialautomation components 20 (e.g., machine components) that, for example,fill, label, package, or palletize the beverage containers.Additionally, the packaging factory 50 may include automation components20 (e.g., one or more conveyor sections) that, for example, transport,align, or buffer containers between the machine components. AlthoughFIG. 4 illustrates a packaging factory, it should be noted that theembodiments described herein are not limited for use with a packagingfactory. Instead, it should be understood that the embodiments describedherein may be employed in any industrial automation environment.

As described above, the packaging factory 50 may subdivided into areas16, cells 18, and components 20. More specifically, the areas 16 may becategorized based on the production process performed. For example, inthe depicted embodiment, the factory 50 may divided into area 1, whichcorresponds with a preparation process, and area 2, which correspondswith a packaging process. Furthermore, the areas 16 may be furtherdivided into cells 18 based on the function (e.g., aspect) performed inthe production process. For example, in the depicted embodiment, area 1may be divided into cell 1, which corresponds to a loading function,cell 2, which corresponds with a washing function, and cell 3, whichcorresponds with a filling and sealing function. Additionally, area 2may be divided into cell 4, which corresponds with a sterilizationfunction, cell 5, which corresponds with a labeling function, and cell6, which corresponds with a packaging function. Moreover, as describedabove, to facilitate each particular function, each cell may include oneor more industrial automation components 20.

To help illustrate, one embodiment of the operation of the packagingfactor 50 is described. For example, the preparation process may beginat cell 1, which as depicted includes loading components 52. Morespecifically, the loading components 52 feed pallets of empty cans orbottles into the packaging factory 50. From cell 1, the bottles may moveon to cell 2, which as depicted includes a conveyer component 54, anindustrial automation power component 78, and washing components 56.More specifically, the power component 78 (e.g., a variable speed motordrive) may supply power to a motor to actuate the conveyor component 54,which transports the empty bottles from the loading components 52 to thewashing components 56, where the empty cans and bottles are washed andprepared for filling. From cell 2, the washed bottles move on to cell 3,which as depicted includes an aligning conveyor component 58 and fillingand sealing components 60. More specifically, as the washed bottles exitthe washing components 56, the conveyor component 54 may graduallytransition into an aligning conveyor component 58 to feed the bottles tothe filling and sealing components 60 in single-file.

From the preparation process, the bottles then move to the packagingprocess (e.g., area 2). The preparation process may begin at cell 5,which includes a buffering conveyer component 62, an industrial powercomponent 78, and labeling components 60. More specifically, as thesealed bottles exit the filling and sealing components 60, the bufferingconveyor component 62 may hold the sealed cans to delay their entry intothe next cell. Thus, the power component 78 (e.g., a variable speedmotor drive) may supply power to a motor to actuate the bufferingconveyer component 62, which may transport the sealed bottles to thelabeling components 60, where the bottles are labeled, for example, witha company logo. Additionally or alternatively, the preparation processmay begin at cell 4, which includes the buffering conveyer component 62,an industrial power component 78, and sterilization components 64. Morespecifically, the power component 78 (e.g., a variable speed motordrive) may supply power to a motor to actuate the buffering conveyorcomponent 62, which may transport the sealed bottles to thesterilization components 64, where the bottles are sterilized, forexample using ultraviolet light irradiation. In the depicted embodiment,as the bottles exit the sterilization components 64, they may betransported to cell 5 (e.g., labeling components 60). From cell 5, thelabeled bottles move to cell 6, which as depicted includes packagingcomponents 68. More specifically, after the cans and bottles have beensterilized and/or labeled, the packaging components 68 may package thebottles into cases (e.g., 6-pack, 24-pack, etc.) before they arepalletized for transport at station 70 or stored in a warehouse 72. Ascan be appreciated, for other applications, the industrial automationcomponents 20 may be different and specially adapted to the application.

Furthermore, as depicted, the packaging factory 50 also includes theindustrial control system 22, which may be located in a control room 74or the like. Additionally, as described above, the control system 22 mayreceive feedback information (e.g., operational parameters) from varioussensors 76 around the packaging factory 50. More specifically, thesensors 76 may measure parameter values of interest relating to thebeverage packaging process, such as the speed of a conveyor component 54or the electric power supplied to a power component 78. Accordingly, asin the depicted embodiment, the sensors 76 may be located in variouspositions around the packaging factory 50. For example, a motion sensor76 may be included with the conveyor component 54 to measure the rate atwhich the bottles are proceeding through the packaging factory 50.Furthermore, sensors 76 may be included in the power components 78 tomeasure the power supplied to the power component 78. Accordingly, thecontrol system 22 may determine the energy usage of a power component 78based on the power usage over time. Additionally or alternatively,sensors 76 may measure other operational parameters that enable thecontrol system 22 to determine energy usage by one or more components20. For example, integrating the current supplied to a pump driving achemical pump may provide a good first order estimate of the energyused.

Moreover, the factory 50 may include one or more utility meters 80(e.g., sensors 76). In some embodiments, one utility meter 80 maymeasure the energy usage by the entire factory 50. Additionally oralternatively, multiple utility meters 80 may be included to monitor theenergy usage of a particular area 16 (e.g., area 1 or area 2) or cell 18(e.g., cells 1-6). For example a first utility meter may measure theutility usage of area 1 and a second utility meter may measure theutility usage of cell 5. Accordingly, the control system 22 maydetermine the energy usage of a cell 18, an area 16, and/or the factory50 based on the utility meter measurements.

Energy Usage Inference Engine

As described above, the industrial control system 22 may controloperation of the industrial automation system 10 based at least in parton the operational parameters of the system. For example, the controlsystem 22 may instruct the power device 78 to slow down the conveyersection 54 in order to reduce energy usage. Thus, the control system 22may determine operational parameters for each component 20, each cell18, each area 16, and each factory 14. More specifically, theoperational parameters may include energy usage/consumption, productmixes, product recipes, operating setpoints (e.g., motor speeds,tensions, oven temperature, and conveyor speeds), production run rates,production schedules, product routing, and control algorithms.

In one embodiment, to determine the energy usage of each component 20, asensor 76 may be placed at every component 20. However, as describedabove, including sensors in the industrial automation system 10 mayincrease the operating cost of the system. Thus, in some embodiments,sensors 76 may be included only at selected components, such as thepower components 78.

Accordingly, to facilitate controlling operation of the industrialcontrol system, the control system 22 may infer some operationalparameters based on measured operational parameters. For example, thecontrol system 22 may infer a rate at which the bottles are entering thewashing components 56 (e.g., product throughput) based on a speed of theconveyer component 54, which may be measured by a motion sensor 76.Similarly, the control system 22 may infer the energy usage of a firstcomponent based on the power supplied to a second component, which maybe measured by a power sensor 76.

One embodiment of a process 82 for determining the energy usage of acomponent or a group of components is described in FIG. 5. The process82 may be implemented via machine-readable instructions stored in thetangible non-transitory memory 36 and/or other memories and executed viaprocessor 34 and/or other processors. Generally, the process 82 includesdetermining a known power usage (process block 84), inferring an unknownpower usage based on the known power usage (process block 86), anddetermining the energy usage based on the inferred power usage (processblock 88).

To help illustrate, the process will be described in relation to thecell 89 depicted in FIG. 6. The described example is merely illustrativeand one or ordinary skill in the art will be able to expand thedescribed techniques to a single component 20, an area 16, a factory 12or 14, or the entire industrial automation system 10. As depicted, thecell 18 includes a controller 90, a first motor drive 92 that drives afirst motor 94, a second motor drive 96 that drives a second motor 98, athird motor drive 100 that drives a third motor 102, and an I/O chassis104, which enables the cell 89 to communicate with other components 20,the control system 22, a backplane, or a network.

As described above, sensors 76 may be used to determine the powersupplied to a component 20 or a group of components 20. For example, inthe depicted embodiment, a first sensor 106 is included in the firstmotor drive 92, a second sensor 108 is included in the second motordrive 96, a third sensor 110 is included in the third motor drive 100,and a fourth sensor 112 is placed before the controller 90. Thus, thefirst sensor 106 may measure the total power supplied to the first motordrive 92. More specifically, the power supplied to the first motor drive92 is partially used by first motor drive 92 and partially supplied tofirst motor 94. Accordingly, the amount of power supplied to the firstmotor 94 (e.g., used by the first motor 94) may be determined bysubtracting the power used by the first motor drive 92 from the totalpower supplied (e.g., measured by the first sensor 106).

In some embodiments, the power used by the motor drive and the motor maybe determined using a model to simulate operation of the motor drive andthe motor. For instance, the model of the motor drive may describe therelationship between power usage and operational parameters of the motordrive, such as product being produced, time of day, operators on duty,environmental conditions, materials being used, product mix, operatingconditions, production run rates, production schedules, product routing,control algorithms and the like. As will be described in more detailbelow, the model of the motor drive or motor may be based on principlesof physics, specification information from a drive manufacturer,specification information for a motor manufacturer, empirical testing,previous operation of the motor drive or motor, or any combinationthereof

In other words, the power usage of the first motor 94 may be inferredbased on the amount of power measured by the first sensor 106 and theamount of power expected to be used by the first motor drive 92.Additionally, the power used by the second motor drive 96, the secondmotor 98 and the third motor 102 may be determined in a similar manner.Additionally or alternatively, as described above, the sensors maymeasure other operational parameters that enable the power usage and/orenergy usage of any one of the components 20 to be determined.

Furthermore, the fourth sensor 112 may measure a total amount of powersupplied to the cell 89 that corresponds to the total amount of powerused by the various components 20 in the cell 89. For example, in thedepicted embodiment, the total amount of power supplied to the cell 89is used by the controller 90, the first motor drive 92, the first motor94, the second motor drive 96, the second motor 98, the third motordrive 100, the third motor 102, and the I/O chassis 104. As describedabove, the amount of power used by the motor drives and motors may bedetermined by the sensors 106-110 or using a model of the motor drivesand motors. In one example, the power used by the controller 90 and theI/O chassis 104 may be determined by subtracting the power measured bythe first sensor 106, the second sensor 108 and the third sensor 110from the amount of power measured by the fourth sensor 112.

Additionally, the amount of power supplied to the I/O chassis 104 may bedetermined by subtracting out the amount of power used by the controller90. In some embodiments, similar to the motor drive, the power used bythe controller 90 may be determined using a model to simulate operationof the controller 90. As such, the model of the controller 90 maydescribe the relationship between power usage and operational parametersof the controller 90. In other words, the power usage of the I/O chassis104 may be inferred based on the amount of power measured by the sensors106-112 and the amount of power used by the controller 90.

As mentioned above, energy usage by each motor drive and motor may beinferred using models of the motor drives and motors along with energyconsumed by the I/O interface 104, which may be inferred using a modelof the controller 90. In other words, more generally, the models thatsimulate operation of a component 20 may be used to infer energy usageby another component 20. In some embodiments, the use of models to inferenergy usage enables the energy usage to be determined in real-time ornear real time. More specifically, although there may be somecomputation involved in using the models, improvements in computingpower may enable the calculation of inferred energy usage to be almostinstantaneous. In other words, the inferred energy usage by a firstcomponent may be viewed at substantially the same time as the measuredenergy usage by a second component.

To further improve the speed (e.g., real-time nature) of inferringenergy usage, the complexity of the model may be adjusted. For example,a simpler (e.g., steady-state) model may be used to improve speed and amore complex model (e.g., a dynamic model) may be used to improveaccuracy. More specifically, the complexity of the model may be adjustedby adjusting the operational parameters used, the number of operationalparameters used, the order of the model, the type of model, or anycombination thereof. In other words, multiple models for a singlecomponent 20 may be used and the model that is used may be based on thespeed and accuracy desired for a particular function.

Additionally, depending on the component 20 being modeled, the model maybe any suitable parametric model that simulates operation of thecomponents, such as a parametric empirical model, a parametricmathematical model, a parametric theoretical model, a parametricfirst-principles model, a parametric hybrid model, or the like.Furthermore, any suitable modeling techniques may be utilized togenerate the model of a component 20.

To help illustrate, non-limiting examples for generating a model of acomponent 20 are described. Generally, the model may be generated basedon known information, for example, from principles of physics,specification information from a manufacturer of the component 20 or arelated component 20, previous operation of the component 20, or anycombination thereof.

In a first example, the manufacturer specifications for the controller90 may describe the internal components in the controller 90 and thedesigned power usage for the internal components. As such, determiningwhich internal components are operating and what operations they areperforming under a set of operational parameters (e.g., a particularcontrol action) enables the power usage to be determined. In someembodiments, which internal components are operating and what operationsare performing may be determined by looking at the instructions executedby the controller 90 and, more specifically, what they instruct thecontroller 90 to perform.

In a second example, the manufacturer specifications for the motor drivemay include the designed power usage by the motor drive to drive a motorunder specific operational parameters (e.g., specific horsepowerlevels). As such, determining the horsepower level at which the motor isbeing run enables the power usage by the motor drive to be determined.In some embodiments, the operational parameters may be measured usingsensors. For example, a sensor may measure the speed, torque, orhorsepower at which the motor is actuating.

As can be appreciated, the manufacturer specifications may not detailthe designed power usage for every possible set of operationalparameters. In other words, other operational parameters, such asproduct being produced, time of day, environmental conditions,production schedule, or materials being used, may affect the power usageof the modeled component 20. For example, ambient temperature around themotor and motor drive may cause the motor drive to use a differentamount of power than in the specification. Similarly, the duration andfrequency (e.g., production schedule) the motor drive is operating maycause the motor drive to use a different amount of power than in thespecification.

To help account for the different possible variations in operationalparameters, empirical testing may be used. For example, empiricaltesting may be used to determine how operational parameters may affectpower usage by the modeled component. In fact, in some embodiments,empirical testing may by itself enable the determination of therelationship between operational parameters and power usage. Forexample, a temporary sensor may be placed between the motor drive andthe motor to determine the actual amount of power used by each.Additionally, other sensors may measure the operational parametersassociated with the motor drive and motor. Based on the measured powerusage and the corresponding operational parameters, a model may begenerated to describe the empirically determined relationship.

In other words, the empirical testing may use a calibration sequence ofmeasuring the actual power usage of a component 20 and the correspondingoperational parameters. More specifically, in some embodiments, thecalibration sequence may be performed when the component 20 iscommissioned. In such embodiments, the calibration sequence may operatethe component 20 with operational parameters that are expected to beexperienced and measure the power usage by the component 20, forexample, with a temporary sensor. In other embodiments, the calibrationsequence may be based on the previous normal operation of the component20. In such embodiments, the model of the component 20 may be generatedbased on operational parameters that have actually been experienced bythe component 20 and may likely occur again. For example, the energyusage patterns associated with producing a specific product shouldremain relatively consistent. In other words, the models may begenerated based on patterns of operation by the component.

As can be appreciated, in other embodiments, the number and/or types ofcomponents 20 included in the cell 89 may differ. For example, thedepicted I/O chassis 104 may be replaced with ten I/O chassis. In suchan embodiment, the combined power usage of the ten I/O chassis may bedetermined in the manner described above, for example, by subtractingthe power usage by the controller 90 and the power usage measured by thefirst sensor 106, second sensor 108, and the third sensor 110 from thetotal power usage measured by the fourth sensor 112. In someembodiments, the power usage of each individual I/O chassis may bedetermined by dividing the combined power usage equally among the I/Ochassis. For example, the power usage of one of the ten I/O chassis maybe determined by dividing the combined power usage by ten. However, inother embodiments, if the combined power usage of the plurality of I/Ochassis is not significant with respect to the other components (e.g.,motor drives, motor, and controller), for example one-tenth of the totalpower usage, the depicted metering granularity may be sufficient. Inother words, it may be sufficient to group together the power usage of aplurality of components (e.g., combined power usage).

Based on the techniques described above, the power usage of all eightcomponents 20 in the cell 89 may be determined (e.g., measured orinferred) through the use of four sensors. Accordingly, the energy usageof the components 20 may be determined based on the power usage overtime. One embodiment of a process 114 for determining the energy usageof the components 20 is described in FIG. 7. Generally, the process 114includes measuring the total power supplied to the cell 89 (processblock 116), measuring the power used by each motor drive and motor pair(process block 118), determining the power used by each motor drive(process block 120), determining the power used by each motor (processblock 122), determining the power used by the controller (process block124), determining the power used by the I/O chassis (process block 126),and estimating energy used by each component (process block 128).Additionally, the process 114 may optionally include determining theactual energy used by each component (process block 130) and updatingmodel(s) accordingly (process block 132). Although the process 114 isdescribed with reference to the cell 89 of FIG. 8, it should be notedthat the process 14 may be performed with other groups of components.The process 114 may be implemented via machine-readable instructionsstored in the tangible non-transitory memory 36 and/or other memoriesand executed via processor 34 and/or other processors.

Accordingly, in some embodiments, the control system 22 may determinethe total amount of power supplied to the cell 89 (process block 116).More specifically, the control system 22 may receive a sensor readingfrom the fourth sensor 112 via the I/O ports 40, and the processor 34may determine the supplied power based on the sensor reading. Forexample, in some embodiments, the sensor reading may be a powermeasurement. Accordingly, the processor 34 may interpret the sensorreading to determine the power supplied to the cell 89. In otherembodiments, the sensor reading may include a current and/or a voltagemeasurement. Accordingly, in such embodiments, the processor 34 mayinterpret the sensors readings and calculate the supplied power. Forexample, the processor 34 may multiply a current measurement and avoltage measurement to calculate the power supplied to the cell 89.

Additionally, the control system 22 may determine the power supplied toeach motor drive and motor pair (process block 118). More specifically,the control system 22 may receive sensor readings from the first sensor106, the second sensor 108, and the third sensor 110 via the I/O ports40. Based on the sensor readings from the first sensor 106, the controlsystem 22 may determine the power supplied to the first motor drive 92and first motor 94 pair. Similarly, the processor 34 may determine thepower supplied to the second motor drive 96 and second motor 98 pairbased on the sensor readings from the second sensor 108, and the controlsystem 22 may determine the power supplied to the third motor drive 100and the third motor 102 pair based on the sensor readings from the thirdsensor 110.

As described, the control system 22 may also determine how the powersupplied to each motor drive and motor pair is divided. Accordingly, thecontrol system 22 may determine the amount of power used by each motordrive with a model of the motor drive based on principles of physics,manufacturer specifications, empirical testing and/or previous operationof the motor drive (process block 120). In some embodiments, themanufacturer specifications relating to the motor drive may identify thetypes of internal components included in the motor drive, the expectedpower usage by each internal component, the expected power usage of themotor drive as a whole, or the like. Additionally, in some embodiments,energy usage may be measured in empirical testing under various sets ofoperational parameters. Accordingly, a model that simulates the expectedpower usage by the internal components of the motor drive duringparticular operations may be created and stored in memory 36.

In some embodiments, the control system 22 may determine the amount ofpower used by a motor drive by retrieving the model from the memory 36and inputting the operational parameters of the motor drive into themodel. For example, the the control system 22 may use the model todetermine that the first motor drive 92 uses 200 watts of power to drivethe first motor 94 at five horsepower (e.g., operational parameter). Asdescribed above, various operational parameters from sensors 76 placedaround the industrial automation system 10 may be fed back to thecontrol system 22.

Based on the power used by each motor drive, the control system 22 maydetermine the amount of power used by each motor (process block 122).More specifically, the control system 22 may determine the power used bya motor by subtracting the power used by a motor drive from the powermeasured by the sensor in the motor drive. For example, based on sensorreadings received from the first sensor 106, the control system 22 maydetermine that 500 watts of power are supplied to the first motor drive92 and the first motor 94 when the first motor drive 92 drives the firstmotor 94 at five horsepower. Continuing with the above example, thecontrol system 22 may determine that the amount of power used by thefirst motor 94 is 300 watts by subtracting 200 watts (e.g., power usedinternal electronics of first motor drive 92) from the 500 watts (e.g.,power measured by first sensor 106).

Similar to determining the power used by each motor drive, the controlsystem 22 may determine the power used by the controller 90 with a modelof the controller 90 based on principles of physics, specificationinformation from the controller manufacturer, and/or previous operationof the controller 90 (process block 124). In some embodiments, themanufacturer specification may provide the general power usage by thecontroller 90. Accordingly, a model that simulates the expected powerusage by the controller 90 during particular operations may be createdand stored in memory 36.

Thus, in some embodiments, the control system 22 may determine theamount of power used by the controller 90 by retrieving the controllermodel from the memory 36 and inputting the operational parameters intothe model. Based on the power used by the controller 90, the controlsystem 22 may determine the power used by the I/O chassis 104 (processblock 126). More specifically, the control system 22 may determine thepower used by the I/O chassis 104 by subtracting the power used by thecontroller 90, the first motor drive 92, the first motor 94, the secondmotor drive 96, the second motor 98, the third motor drive 100, and thethird motor 102. In other words, the control system 22 may determine thepower used by the I/O chassis 104 by subtracting the determined amountof power used by the controller 90, the power measured by the firstsensor 106, the power measured by the second sensor 108, and the powermeasured by the third sensor 110 from the total power measured by thefourth sensor 112. For example, continuing with the above example, ifthe first, second, and third sensors each measures 500 watts of powerand the fourth sensor 112 measures 2000 watts of power, the processor 34may determine that the amount of power used by the I/O chassis 104 is200 watts by subtracting 1500 watts (e.g., power used by motor drivesand motors) and 300 watts (e.g., power used by the controller) from the2000 watts (e.g., total supplied power).

Thus, the control system 22 may determine (e.g., measure or infer) thepower used by each component 20 in the cell 89. Accordingly, the controlsystem 22 may estimate the energy used by each component 20 based on thepower used by each component 20 over time (process block 128). Morespecifically, the processor 34 may integrate the power used by aparticular component 20 over a given time period to determine the energyused by that component 20. Thus, to facilitate the integral calculation,the determined power usage for each component 20 may be stored in memory36 and/or another storage device, such as a cloud computing system. Insome embodiments, the power usage for each component 20 may becontinuously determined and stored. Additionally or alternatively, thepower usage for each component 20 may be periodically determined andstored (e.g., at discrete intervals).

Optionally, the control system 22 may determine the actual energy usedby each component 20 and compare the measured usage with the estimatedenergy usage (process block 130). The actual energy usage may bedetermined though any suitable method. For example, as discussed above,additional sensors 76 (e.g., temporary) may be placed around theindustrial automation system 10 to measure power usage at a moregranular level (e.g., on each individual component 20).

Based on the comparison between the measured usage and the estimatedenergy usage, the control system 22 may update or verify the models usedto infer power usage by the components (process block 132). Morespecifically, the processor 34 may update the models so that theestimated energy usage will more closely approximate the actual energyusage of the components. Thus, the additional sensors 76 may be placedon specific components 20 that enable the actual energy usage of amodeled component to be measured. For example, referring back to FIG. 6,a fifth sensor 134 may be included between the first motor drive 92 andthe first motor 94 to measure the power supplied to the first motor 94(e.g., power usage of first motor 94). Accordingly, the power used bythe first motor drive 92 may be determined by subtracting the powermeasured by the fifth sensor 134 from the power measured by the firstsensor 106, which enables the control system 22 to determine the actualenergy used by the first motor drive 92, the first motor 94, or both.

As described above, the inclusion of additional sensors 76 may increasethe operating cost of the industrial automation system 10. Accordingly,in some embodiments, the additional sensors may be temporary sensorsthat are moved to different parts of the industrial automation system10. For example, the fifth sensor 134 may first be placed between thefirst motor drive 92 and the first motor 94 to verify or update themodel of the first motor drive. Subsequently, the fifth sensor 134 maybe place between the second motor drive 96 and the second motor 98 toverify or update the model of the second motor drive, and so on for theother modeled components.

Clearly, the described embodiment of process 114 is not intended to belimiting. Instead, the description of process 114 is intended to beillustrative of techniques that may be utilized to infer the energyusage of components 20 without directly metering each component 20. Inother words, the techniques utilized in may be adapted to other cellconfigurations as well as with different levels of granularity, such ason a component level, an area level, or a factory level. For example, inan alternative embodiment of the cell 89 described, the fourth sensor112 may instead be placed at the I/O chassis 104. In other words, thetotal energy usage by the cell 89 is not directly measured. As such, thetotal energy usage by the cell 89 may be inferred based on the sensormeasurements and the model of the controller 90.

Additionally, on a component level, the techniques described may be usedto determine the energy usage of different parts that make up acomponent 20 without directly measuring the power usage of each part. Inother words, a control system 22 (e.g., a controller) may control andmonitor energy usage by different parts in an individual component 20.

As will be described in more detail below, determining the energy usageat varying levels may enable various diagnostic tools. For example, ifit is determined that a particular component 20 is using more energythan expected, the component 20 may be identified as a potentiallyfaulty component. Additionally, understanding the energy usage mayenable adjustments in the design and/or operation of one or morecomponents.

In addition to determining the energy usage of individual hardwarecomponents, process 82 may be utilized to determine the energy usage foreach stage in a production process and/or the production process as awhole. As described above, cells 18 may perform an aspect (e.g., astage) of the production process. In other words, the techniquesdescribed herein may be utilized to determine the energy usage of agroup of components 20. To help illustrate, the process 82 will bedescribed in relation to the production process 136 depicted in FIG. 8.More specifically, the production process 136 is the preparation processdescribed above. Accordingly, the production process 136 includes aloading stage 138, a washing stage 140, and a sealing stage 142. As usedherein, the loading stage 138 includes the components of cell 1 (e.g.,loading components 52), the washing stage 140 includes the components ofcell 2 (e.g., conveyer component 54, washing components 56, and powercomponent 78), and the sealing stage 142 includes the components of cell3 (e.g., sealing components 58).

To facilitate determining the energy usage by the production process136, a process model may be developed that describes the power usage ateach stage. More specifically, similar to an individual component 20,the process model may be developed based on the manufacturerspecifications for the components 20 included in production process 136,principles of physics, empirical testing, and/or previous operation ofeach stage. For example, since the manufacturer specifications maydescribe the expected power usage by each individual component 20, theexpected power usage may be combined into the process model.Additionally, the previous energy usage by each stage along with thestate of the stage and the control actions performed may be used togenerate the model. In other words, the process model may simulateoperation of a process stage to describe the relationship betweenoperational parameters of the process stage and the energy or powerusage.

Accordingly, the process model may enable energy usage by a stage to beestimated based on the control actions performed and/or the state of thestage. For example, based on the current state of the production process136, the process model may determine the amount of energy that will beused to achieve a new setpoint speed or temperature. The developedprocess model may be stored in the memory 36 and/or another storagedevice accessible by the control system 22, such as the cloud.

To improve accuracy, the actual power usage may be measured and used toupdate and/or verify the process model. As described above, sensors 76may measure the operational parameters of the production process 136,such as the power supplied to a stage. For example, in the depictedembodiment, a first sensor 144 is included in the loading stage 138 anda second sensor 146 is included in the sealing stage 142. Accordingly,the first sensor 144 may measure the power supplied to the loading stage138 (e.g., power used by the components 20 in the loading stage 138) andthe second sensor 146 may measure the power supplied to the sealingstage 142 (e.g., power used by the components 20 in the sealing stage142). More specifically, in some embodiments, the sensors 144 and 146may be placed similar to the fourth sensor 112 described above.Additionally or alternatively, as described above, the sensors 144 and146 may measure other operational parameters that enable the energyusage to be determined.

On the other hand, as depicted in FIG. 8, since a sensor is not includedin the washing stage 140, to facilitate determining the energy usage,the power supplied to the washing stage 140 may be determined (e.g.,inferred) using various techniques described herein. For example, atemporary sensor may be placed in the washing stage 140 to directlymeasure the power usage and generate a process model for the washingstage 140 that describes the relationship between power usage andoperational parameters of the washing stage 140. Additionally oralternatively, an upstream sensor may measure the total power suppliedto the production process 136 to generate a process model for theproduction process 136 as a whole. The process model for the productionprocess 136 may thus describe the relationship between power usage andoperation parameters of the production process 136.

Accordingly, the power supplied to the washing stage 140 may bedetermined by subtracting the power measured by the first sensor 144 andsecond sensor 146 from the power usage determined using the processmodel of the production process 136. In other words, the power suppliedto the washing stage 140 may be inferred based on the power measured bythe sensors 144 and 146 and the process model of the production process136.

Based on the techniques described above, the power usage of each of thestages in the production process 136 may be determined (e.g., measuredor inferred). Accordingly, the energy usage by each stage and/or energyusage by the production process 136 as a whole may be determined byintegrating the power usage over time. One embodiment of a process 148for determining the energy usage for each stage and the productionprocess 136 as a whole is described in FIG. 9. Generally, the process148 includes estimating power usage based on a process model (processblock 150), measuring the power supplied to the loading stage and thesealing stage (process block 152), and determining the energy usage byeach stage (process block 154). Additionally, process 148 optionallyincludes determining actual energy usage (process block 155) andupdating or verifying the process model (process block 156). Althoughthe process 148 is described with reference to the production process136 of FIG. 8, it should be noted that the process 148 may be performedwith other production processes. The process 148 may be implemented viamachine-readable instructions stored in the tangible non-transitorymemory 36 and/or other memories and executed via processor 34 and/orother processors.

Accordingly, in some embodiments, the control system 22 may estimate theamount of power that will be used based on a process model (processblock 150). As described above, the process model may be developed todescribe the power usage by the production process 136 as a whole and/oreach individual stage in the production process 136. For example, theprocess model may simulate operation of the production process 136 basedon operational parameters of the production process 136. In other words,the process model for the production process 136 may describe therelationship between operational parameters and power usage.Accordingly, the processor 34 may access the process model from memory36 and determine an estimate of the power that will be used by theproduction process 136 by inputting one or more operational parameters(e.g., control actions or setpoints) into the process model.

To facilitate determining power usage by the washing station 140, thecontrol system 22 may measure the power supplied to the loading stage138 and the sealing stage 142 (process block 152). In some embodiments,the control system 22 may receive sensor readings from the first sensor144, the second sensor 146, and any other sensors included in theproduction process, such as temporary sensors and/or upstream sensors,via the I/O ports 40. Based on the sensor readings from the first sensor144, the processor 34 may determine the power supplied to the loadingstage 138. Similarly, the processor 34 may determine the power suppliedto the sealing stage 142 based on the sensor readings from the secondsensor 146.

Based on the power measurements, the processor 34 may determine thepower supplied to the washing stage 140 using various techniques. Forexample, when the process model describes power usage as a whole, theprocessor 34 may infer the power used by the washing stage 140 bysubtracting the power measured by the first sensor 144 and second sensor146 from the power usage estimated by the process model. Additionally oralternatively, an upstream sensor may be used to measure power usage bythe production process 136 as a whole. In other embodiments, a processmodel may simulate operation of the washing stage 140 and determinepower usage by the washing stage 140 based on operational parameters ofthe washing stage 140.

Based on the power used by each stage, the control system 22 maydetermine the energy used by the production process 136 based on thepower usage over time (process block 154). More specifically, thecontrol system 22 may integrate the power used by each component over agiven time period to determine the energy used by that stage. Thus, tofacilitate the integral calculation, the determined power usage for eachstage may be stored in memory 36 and/or another storage device, such asthe cloud. In some embodiments, the power usage for each stage may becontinuously determined and stored. Additionally or alternatively, thepower usage for each stage may be periodically determined and stored(e.g., at discrete intervals).

Optionally, the control system 22 may determine the actual energy usageby each stage and compare the measured usage with the estimated energyusage (process block 154). The actual energy usage may be determinedthough any suitable method. For example, as discussed above, additionalsensors 76 (e.g., temporary) may be placed around the industrialautomation system 10 to measure power usage at a more granular level(e.g., on the washing stage 140 or upstream from the production process136). For example, when the additional sensor is placed upstream fromthe production process 136, the actual power usage by the productionprocess 136 and the washing station 140 may be measured. Thus, theactual energy usage may be determined by integrating the actual powerusage over time.

Based on the comparison, the control system 22 may update or verify theprocess model used to infer power usage (process block 156). In someembodiments, the processor 34 may update the process model so that theestimated energy usage will more closely approximate the actual energyusage of each stage or the production process 136 as a whole.Additionally or alternatively, the processor 34 may update or verify theprocess model based on a comparison of the estimated energy usage andthe actual energy usage for each stage.

In further embodiments, the control system 22 may update or verify theprocess model by comparing actual (e.g., measured) power usage with thepower usage estimated by the process model. In some embodiments, thecontrol system 22 may update the process model so that the estimatedpower usage will more closely approximate the actual power usage of eachstage or the production process 136 as a whole. Additionally oralternatively, the processor 34 may update or verify the process modelbased on a comparison of the estimated power usage and the actual powerusage for each stage.

As illustrated in the above examples, sensors 76 are strategicallyplaced such that the energy usage by each component 20 or group ofcomponents (e.g., a cell 18, an area 16, a factory, a stage, or aproduction process) may be determined (e.g., measured or inferred). Insome embodiments, to facilitate the placement of the sensors 76, thecontrol system 22 may analyze the automation system or productionprocess to recommend where to place sensors 76 and/or what sensors 76 touse.

To help illustrate, a non-limiting example is described. Morespecifically, the control system 22 may first determine what sensors 76are currently in place (e.g., location and/or type). In someembodiments, this may include polling each component 20. In otherembodiments, an operator may manually input the information. Based onthe sensor information, the control system 22 may recommend additionalsensors 76 to include to achieve the desired level of energy usagegranularity. For example, referring again to the cell 89 described inFIG. 6, the control system 22 may recommend the placement of a powersensor (e.g., type) upstream from the controller 90 (e.g., location) sothat energy usage for each of the downstream components 20 may bedetermined.

Additionally, as illustrated in the above examples, the energy usage ina component 20 or a group of components (e.g., a cell 18, an area 16, afactory, a stage, or a production process) may be determined. Moreover,the energy usage may be determined on various levels of granularity(e.g., component level, cell level, stage level, area level, or factorylevel). As will be described in more detail below, determining theenergy usage of the production process 136 and each stage may enablediagnostics. For example, if it is determined that a particular stage isusing more energy than expected, the components 20 in the stage may beidentified as potentially faulty and requiring maintenance.Additionally, understanding the energy usage may enable adjustments inthe design and/or operation of one or more components.

Clearly, the described embodiment of process 148 is not intended to belimiting. Instead, the description of process 148 is merely intended tobe illustrative of techniques that may be utilized to determine (e.g.,infer) the energy usage of a production process and each stage in theproduction process without directly metering each stage. In other words,the techniques utilized in may be adapted to other production processes.

The techniques described herein may be expanded beyond energy usage tofurther quantify energy in the industrial automation system 10. Forexample, in each of the embodiments described above, the energy used bythe components 20 (e.g., a stage) is described as being determined.However, in real-world situations, energy losses may cause the amount ofenergy consumed to be different from the amount of energy used. Morespecifically, energy losses may result from conductive or radiativelosses, windage losses, frictional losses, and the like (e.g., waste).In other words, as used herein, “energy consumed” in intended todescribe the total amount of energy supplied and “energy usage” isintended to describe the total of amount of energy that is used by thecomponents 20 to perform a control action or achieve a setpoint. Thus,the amount of energy consumed may be more than the amount of energyactually used by the components 20. Accordingly, each of the techniquesdescribed herein may be adapted to further take into account the amountof energy consumed versus the amount of energy actually used.

One embodiment of a process 158 for determining the amount of energyused is described in FIG. 10. Generally the process 158 includesdetermining energy consumed (process block 160), determining the energylosses (process block 162), and determining the actual energy used(process block 164). The process 158 may be implemented viamachine-readable instructions stored in the tangible non-transitorymemory 36 and/or other memories and executed via processor 34 and/orother processors.

As described above, the process 158 may be utilized to account for theamount of energy consumed versus the amount of energy actually used. Tohelp illustrate, the process 158 will be described in relation to thecell 89 above. As can be appreciated, energy losses in cell 89 maygenerally result from various causes, such as resistance in the cabling(e.g., wires) that carries electricity between the components 20. Forexample, an energy loss may result in the connection between the firstmotor drive 92 and the first motor 94. Similarly, an energy loss mayresult in the connection between the controller 90 and each of the firstmotor drive 92, the second motor drive 96, the third motor drive 100,and the I/O chassis 104. Additionally, an energy loss may result fromfriction in the motors and losses in the windings of the motors.

To help account for the various energy losses, energy loss models may bedeveloped based on principles of physics, manufacturer specifications,and/or previous operations. For example, the manufacturer specificationmay describe the amount of energy loss in one foot of cabling betweenthe first motor drive 92 and the first motor 94. Additionally oralternatively, the energy loss may be calculated based on the materials,circumference, cross-sectional area, and/or other characteristics of thecabling. Furthermore, in some embodiments, temporary sensors 76 may beput in place before and after the cabling to directly measure energyloss in the cabling. Other energy loss models may similarly be developedto model other losses in the cell 89. The models may be stored in memory36 and/or other storage devices accessible by the control system 22.

Although the energy loss models are described as separate models, theymay additionally or alternatively be included in the various models ofthe components 20. For example, the energy loss model describing energyloss between the first motor drive 92 and the first motor 94 may beincluded in the model of the first motor drive described above.

Accordingly, in some embodiments, the control system 22 may determinethe total amount of energy consumed by the cell 89 (process block 160).In other words, the control system 22 may determine the total amount ofenergy supplied to the cell 89. More specifically, in some embodiments,the control system 22 may receive a sensor reading from the fourthsensor 112 via the I/O ports 40 and the control system 22 may determinethe supplied power based on the sensor reading. Accordingly, the controlsystem 22 may estimate the energy consumed by the cell 89 by integratingthe power measured by the fourth sensor 112 over a given time period.

Additionally, the control system 22 may determine the energy losses inthe cell 89 (process block 162). As described above, the energy lossesin the cell 89 may be determined based on principles of physics,manufacturer specifications, and/or previous operation. For example, anenergy loss model may be developed to describe the amount of energy lossexpected to result from the cabling that carries electricity from thefirst motor drive 92 to the first motor 94. More specifically, theenergy loss models may describe the energy loss based on operationalparameters. For example the energy loss model for the cabling betweenthe first motor drive 92 and the first motor 94 may describe the energyloss based on the type of cabling, the material used in the cabling, thecircumference of the cabling, the cross-sectional area of the cabling,temperature of the cabling, placement of the cabling (e.g., bends orrelationship to other components), and the like.

Accordingly, the control system 22 may determine the energy loss in thecell 89 by accessing an energy loss model from memory 36 and inputtingthe relevant operational parameters to the model. Thus, the controlsystem 22 may determine the energy actually used by the cell 89 bysubtracting the energy losses from the total energy consumed (processblock 162).

By separating out the amount of energy actually used, the configurationand/or operation of the cell 89 may be adjusted, for example, tominimize waste (e.g., energy losses). For example, the length of cablingbetween the first motor drive 92 and the first motor 94 may be shortenedto reduce energy loss. Additionally or alternatively, the cabling usedbetween the first motor drive 92 and the first motor 94 may be changedout if an alternative produces better results (e.g., cheaper costwithout substantial increase in energy loss or substantially reductionin energy loss). In other words, as will be described in more detailbelow, quantifying the amount of energy loss may provide further insightinto design and operation considerations.

Clearly, process 158 may be adapted for use in a single component 20 orother groups of components (e.g., a cell 18, an area 16, a factory 12,or a production process). For example, energy loss models may bedeveloped to describe energy losses in the loading components 138, thewashing components 140, and the sealing components 142 of the productionprocess 136 described above. Similar to the example described above, theenergy loss models may be developed based on principles of physics,manufacturer specifications, and/or previous operations.

In addition to quantifying the energy usage (or consumption), energy inthe industrial automation system 10 may be further quantified inrelation to a product as it proceeds through a production process. Morespecifically, an energy balance model may be developed that describesenergy in the input product, energy losses, energy used by theproduction process, energy in the output product, and any other energyin the production process. In other words, the energy balance model mayprovide an aggregate measure of energy in the production process.

To help illustrate, a production process 166 is described in FIG. 11. Asdepicted, an input product 168 is input to and processed by theproduction process 166. After processing, an output product 170 isoutput from the production process 166. More specifically, the energy inthe production process 166 may include the energy included with theinput product 168, the energy consumed by the production process 166,and the energy output with the output product 170. For example, in abaking production process, the input product 168 may have a certainamount of heat (e.g., energy), the baking process 166 may useelectricity (e.g., energy) to bake the product, the output product 170may have a certain amount of residual heat (e.g., energy), and energylosses may consume energy.

As described above, the energy balance model may provide an aggregatemeasure of such energy. In other words, the control system 22 may usethe energy balance model to determine the energy present in theproduction process 166. More specifically, the energy balance model mayoutput the energy present in the production process 166 when operatingaccording to one or more operational parameters. For example, thecontrol system 22 may input a desired production schedule to determinethe aggregate energy present if the desired production schedule isfollowed. Similarly, the control system 22 may input a desired outputproduct 170 quality to determine the aggregate energy present to achievethe desired quality. As will be described in more detail below, theaggregate measure of energy may enable adjustments to the configurationand/or operation of the production process.

Accordingly, to generate the energy balance model, the energy in aproduction process may be tracked in relation to the operationalparameters of the production process. To help illustrate, one embodimentof a process 172 for tracking the energy in the production process 166is described in FIG. 12. Generally, the process 172 includes measuringthe energy in the input product (process block 174), determining theenergy consumed by the production process (process block 176), andmeasuring the energy in the output product (process block 178). Theprocess 172 may be implemented via machine-readable instructions storedin the tangible non-transitory memory 36 and/or other memories andexecuted via processor 34 and/or other processors.

In one embodiment, the control system 22 may determine the energyincluded with the input product 168 (process block 174). Morespecifically, the control system 22 may determine the energy based onreadings from sensors. For example, the control system 22 may determinethe heat (e.g., energy) included with the input product 168 based on areadings from a sensor that measures the temperature of the inputproduct 168. Additionally or alternatively, sensors may be used tomeasure other type of energy included with the input product 168, suchas electrical energy, chemical energy, or mechanical energy.

The control system 22 may also determine the energy consumed by theproduction process 166 (process block 176). In some embodiments, anupstream sensor may be in place to measure the energy consumed by theproduction process 166. In other embodiments, the techniques describedherein may enable the consumed energy to be determined without the useof an upstream sensor. For example, a process model may be used todetermine energy used by the production process 166 and energy lossmodels may be used to determine the energy losses in the productionprocess 166. Thus, by combining the energy used and the energy losses,the control system 22 may determine the energy consumed by theproduction process 166.

Similar to determining the energy input with the input product 168, thecontrol system 22 may determine the energy output with the outputproduct 170 (process block 178). More specifically, the control system22 may determine the energy based on readings from sensors. For example,the control system 22 may determine the heat (e.g., energy) includedwith the output product 170 based on a readings from a sensor thatmeasures the temperature of the output product 170. Additionally oralternatively, sensors may be used to measure other type of energyincluded with the output product 170, such as electrical energy,chemical energy, or mechanical energy.

As described above, the energy balance model may describe the aggregateenergy in the production process 166 for various operational parameters.As such, the control system 22 may generate energy balance model byrepeating process 172 for different operational parameters (e.g.,operating strategies). In other words, the control system 22 may trackthe energy in the production process 166 under different operationalparameters. More specifically, in some embodiments, the control system22 may track the energy during normal operation of the productionprocess 166. As such, the control system 22 may track the energy forrealistic operational parameters that may occur again (e.g., a pattern).Additionally or alternatively, the control system 22 may run a setupsequence that executes the production process 166 with variousoperational parameters.

Based on the techniques described above, energy usage for one or morecomponents may be inferred from energy usage by other components. Assuch, technical effects include enabling the energy information to bedetermined on various levels of granularity, such as a component level,a cell level, an area level, a factory level, or a production processlevel. More specifically, in some embodiments, the number of sensorsused may be reduced by inferring the energy usage of components. Forexample, the energy usage of a component may be inferred by modeling thecomponent and determining energy usage based on the model and energyusage by related components. As such, utilizing the inference enginedescribed above may enable the use of fewer sensors, which results inless maintenance.

Energy Usage Auto-Baseline

Based on the techniques described above, energy in an industrialautomation system 10 may be quantified on multiple levels and throughvarious metrics (e.g., energy usage, energy consumption, process model,and energy balance model). For example, the control system 22 maydetermine the energy usage of one or more components. As describedabove, such energy metrics may facilitate diagnostics and/or prognosticson the industrial control system. More specifically, as will bedescribed in more detail below, the energy usage over time mayfacilitate identifying when a component 20 is potentially faulty and/orpredicting when a maintenance related activity should occur.

In some embodiments, the energy usage over time may be used to generatean energy usage baseline. As used herein, the “energy usage baseline” isintended to describe an expected energy usage range (e.g., threshold orenergy usage profile). Accordingly, for example, when the energy usageby a component 20 falls outside a range/tolerance of the energy usagebaseline, the component 20 may be identified as potentially faulty.

One embodiment of a process 180 for setting an energy usage baseline isdescribed in FIG. 13. Generally, the process 180 includes determiningenergy usage over time (process block 182) and setting the usagebaseline (process block 184). Optionally, process 180 also includessetting an alarm and/or an event (process block 186) and adjusting theenergy usage baseline over time (process block 188). As will bedescribed in more detail below, an energy usage baseline may be set forvarious levels of granularity, such as internal components, components20, cells 18, areas 16, factories, or production processes. The process180 may be implemented via machine-readable instructions stored in thetangible non-transitory memory 36 and/or other memories and executed viaprocessor 34 and/or other processors.

Accordingly, in some embodiments, the control system 22 may determinethe energy usage of one or more components 20 over time (process block182). As discussed above, the control system 22 may determine energyusage through various techniques, such as directly measuring energyusage or inferring energy usage. To facilitate determining the energyusage over time, the control system 22 may store the determined energyusage in memory 36 and/or other storage devices, such as the cloud. Morespecifically, in some embodiments, energy usage may be divided into setsbased on time, state of a component, and/or control actions performed.For example, the energy usage may be divided into the energy usageduring each five minute interval of operation. Additionally oralternatively, the energy usage may be divided into energy usage to washten bottles. As will be described in more detail below, this may enablethe energy usage baseline to more accurately define a range of expectedenergy usage.

Based on the energy usage over time, the control system 22 may set anenergy usage baseline for the one or more components (process block184). As described above, the energy usage baseline may include a rangeof energy usage that is expected for the one or more components. Forexample, the energy usage baseline for a motor drive (e.g., first motordrive 92) may be 400+/−55 kWh. Accordingly, to determine the energyusage baseline, the processor 34 may retrieve the stored energy usageover a period of time from memory 36 and determine an average of thevalues. For example, the control system 22 may determine an average ofthe ten most recently stored energy usage values for the motor drive anddetermine that the average energy usage by the motor drive is 400 kWh.Additionally, the control system 22 may determine a standard deviationof the stored energy usage values to determine a range of expectedenergy usage around the mean. For example, the control system 22 maytake the standard deviation of the ten most recently stored energyvalues for the motor drive and determine that the corresponding standarddeviation is 55 kWh. The energy usage baseline may be stored in memory36, storage 38, or other storage device, such as a cloud storage device.As will be described in more detail below, since the energy usagebaseline describes the expected energy usage, the energy usage baselinemay be facilitate diagnostics and/or prognostics on the one or morecomponents.

In some embodiments, to further improve the diagnostic and/or prognosticfunction of the energy usage baseline, the control system 22 maycorrelate energy usage data to generate the energy usage baselines. Forexample, the control system 22 may identify relationships between thevarious operational parameters, such as a product being produced, a timeof day, operators on duty, environmental conditions, materials beingused, product mix, operating conditions, production run rates,production schedules, product routing, control algorithms, and the like.In general, the energy usage under a particular set of operationalparameters is expected to similar to energy usage in a previous instanceunder a similar set of operational parameters.

As such, separate energy usage baselines based on varying operationalparameters may be used to enable the energy usage baselines to moreaccurately define the expected energy usage. For example, a first energyusage baseline may be set to describe the expected energy usage when amotor drive is actuating a load and a second energy usage baseline maybe set to describe the expected energy usage when the motor drive isidle. Similarly, a first energy usage baseline may be set to describeexpected energy usage when the motor drive is actuating a pump and asecond energy usage baseline may be set to describe expected energyusage when the motor is actuating a fan. Furthermore, a first energyusage baseline may be set to describe expected energy usage when themotor drive operates during the day and a second energy usage baselinemay be set to describe energy usage when the motor drive operates duringthe night.

In some embodiments, the control system 22 may additionally oralternatively utilize an energy consumption baseline. As describedabove, energy usage describes the amount of energy actually used by oneor more components whereas energy consumption describes the total amountof energy consumed by the one or more components, which may include theenergy usage as well as energy waste. Similar to the energy usagebaseline, the energy consumption baseline may be set based on previousenergy consumption.

In some embodiments, the use of the energy consumption baseline mayprovide further insight into diagnostics and/or prognostics. Forexample, even though energy usage by a component may fall within a rangeof the energy usage baseline, the energy consumption by the componentmay fall outside of the energy consumption baseline. In someembodiments, such a situation may facilitate identifying where acomponent is faulty or that a maintenance event is coming up. Forexample, when the acceptable range of the energy usage falls within theenergy usage baseline, the component may be functioning properly, butwhen the energy consumption fall outside of the energy consumptionbaseline, the component may suffer from some problem, such as anenvironmental source of energy waste (e.g., excessive vibration orheat).

Although either or both the energy usage baseline and the energyconsumption baseline may be used, to simplify discussion, the followingwill be directed to the energy usage baseline. However, one of ordinaryskill in the art will understand that the energy consumption baselinemay additionally or alternatively be used.

Once the energy usage baseline is set, the control system 22 may setalarms and/or events based on the energy usage baseline (process block186). More specifically, as will be described in more detail below, thealarms and/or events may notify an operator when energy usage exceeds ornears the energy usage baseline. For example, if the energy usage of amotor drive exceeds its energy usage baseline, an alarm may indicatethat the motor drive is potentially faulty. Additionally oralternatively, if the energy usage by a motor drive approaches theboundaries of its energy usage baselines, an event may be set toindicate that the motor drive may be operating less efficiently. Assuch, the control system 22 may recommend replacing the motor drive.

Additionally, the control system 22 may adjust the energy usage baselineover time (process block 188). More specifically, adjusting the energyusage baseline over time may enable gradual changes in the component 20or components to be taken into account. For example, as a motor driveages, the amount of energy usage by the motor drive may graduallyincrease. Accordingly, since the energy usage baseline may be determinedby averaging previous energy usage, the energy usage baseline may alsogradually increase.

In some embodiments, the number of previous energy usage values to usemay be adjusted to help differentiate between energy usage changes thatresult from gradual aging of components and energy usage changes thatresult from a faulty component. For example, the amount of previousenergy usage values may be increased to reduce the affect a suddenincrease in energy usage will have on the baseline energy usage. On theother hand, the amount of previous energy usage values may be decreasedto increase the adaptability of the baseline energy usage. As such, theenergy usage baseline may automatically adjust over the course ofoperation.

As described above, an energy usage baseline may be used to detect afault in the component 20 or a group of components. One embodiment of aprocess 190 for detecting a fault in a component is described in FIG.14A. Generally, the process 190 includes setting an energy usagebaseline for a component (process block 192), determining when energyusage nears or exceeds the energy usage baseline (process block 194),and detecting a potentially faulty component (process block 196). Theprocess 190 may be implemented via machine-readable instructions storedin the tangible non-transitory memory 36 and/or other memories andexecuted via processor 34 and/or other processors.

In some embodiments, the control system 22 may set the energy usagebaseline for a single component (process block 192). As described above,the energy usage baseline may include a range of energy usage that isexpected for the component (e.g., 400+/−55 kWh). More specifically, thecontrol system 22 may determine expected energy usage based on theenergy usage by the component over time. For example, the control system22 may retrieve store energy usage values from memory 36 and determinean average of the values. Additionally, the processor 34 may determinethe standard deviation of the energy usage values to identify a range ofexpected energy usage. As described above, the energy usage values usedto set the energy usage baseline may be correlated based on variousoperational parameters, such as the product being produced, a time ofday, operators on duty, environmental conditions, materials being used,and the like.

Once the energy usage baseline is set, the control system 22 maycontinue to monitor the energy usage by the component 20 to determinewhen the energy usage nears or exceeds the energy usage baseline(process block 194). More specifically, as the control system 22continues to monitor energy usage, the control system 22 may store theenergy usage values into memory 36. As such, the energy usage baselinemay continue to adapt over time.

When the energy usage nears or exceeds the energy usage baseline, thecontrol system 22 may determine that the component is potentially faulty(process block 196). As described above, the energy usage baselinedescribes the expected amount of energy usage. Accordingly, when energyusage nears or exceeds the energy usage baseline, it is an indicationthat the energy usage is not as expected. Since the energy usage is notas expected, is may be an indication that the component is notfunctioning as expected (e.g., potentially faulty). Additionally, thecontrol system 22 may notify an operator of the potentially faultycomponent, for example, by producing an alarm to alert a user via theoperator interface 24.

However, nearing or exceeding the energy usage baseline merely indicatesthat the component 20 is potentially faulty. In other words, thecomponent 20 may, in fact, not actually be faulty. As such, the controlsystem 22 may notify the operator of a probability that the component 20is actually faulty (e.g., certainty that component is faulty). Thedegree of certainty that the component 20 is actually faulty may bebased on various factors. For example, as described above, the energyusage of the component 20 may be inferred instead of directly measured.Accordingly, the certainty in the determined energy usage may also beaccounted for in the probability that the component 20 is actuallyfaulty.

Additionally, as described above, multiple energy usage baselines may beused. For example, a first energy usage baseline may describe theexpected energy usage based on the all operations performed by thecomponent 20 and a second energy usage baseline may describe theexpected energy usage based on only a specific operation performed bythe component 20. Accordingly, the probability that the component 20 isactually faulty may be based on whether the energy usage near or meetsone or both of the energy usage baselines. For example, the probabilitythat the component 20 is faulty may be less when the energy usageexceeds the second energy usage baseline but not the first energy usagebaseline and may be higher when the energy usage exceeds both the firstand the second energy usage baselines.

Furthermore, the probability that the component 20 is actually faultymay be based on where the determined energy usage falls within theenergy usage baseline or how far the energy usage falls outside of theenergy usage baseline. In other words, the probability of whether thecomponent 20 is faulty may be based on how far the energy usage deviatesfrom the expected energy usage.

As described above, energy usage baselines may be set for varying levelsof granularity. For example, an energy usage baseline may be set forexpected energy usage by a single component, parts of a singlecomponent, or a group of components (e.g., a cell 18, an area 16, or astage in a process). As such, an energy usage baseline may also be usedto detect whether a group of components is potentially faulty. Oneembodiment of a process 198 for determining whether a group of componentis faulty is described in FIG. 14B. Generally, the process 198 includessetting an energy usage baseline for a group of components (processblock 200), determine when energy usage near or exceed the energy usagebaseline (process block 202), and detecting a faulty group of components(process block 204). The process 198 may be implemented viamachine-readable instructions stored in the tangible non-transitorymemory 36 and/or other memories and executed via processor 34 and/orother processors.

In some embodiments, the control system 22 may set the energy usagebaseline for a group of components (process block 200). As with a singlecomponent, the energy usage baseline for a group of components mayinclude a range of energy usage that is expected for the group ofcomponents (e.g., 400+/−55 kWh). More specifically, the control system22 may determine expected energy usage based on the energy usage by thegroup of components over time. For example, the control system 22 mayretrieve store energy usage values from memory 36 and determine anaverage of the values. Additionally, the control system 22 may determinethe standard deviation of the energy usage values to identify a range ofexpected energy usage. As described above, the energy usage values usedto set the energy usage baseline may be correlated based on variousoperational parameters, such as the product being produced, a time ofday, operators on duty, environmental conditions, materials being used,and the like.

Once the energy usage baseline is set, the control system 22 maycontinue to monitor the energy usage by the group of components todetermine when the energy usage nears or exceeds the energy usagebaseline (process block 202). As the control system 22 continues tomonitor energy usage, the control system 22 may store the energy usagevalues into memory 36. As such, the energy usage baseline may continueto adapt over time.

More specifically, in some embodiments, the control system 22 maymonitor energy usage of each individual component in relation to itsrespective energy usage baseline. Additionally or alternatively, inother embodiments, the control system 22 may monitor the energy usage bythe group of components as a whole because it is possible that eventhough each individual component is within its respective energy usagebaseline the combination of the individual components may indicate anunexpected result. As such, the control system 22 may monitor the energyusage by the group of components, for example, by generating a mappingin n-space (e.g., a normative mapping surface) relating inputs tooutputs based on empirical data. The control system 22 may then compareenergy usage by the group of components to the normative mapping surfaceto determine the distance from the normative mapping surface. If theenergy usage is further than a threshold from the normative mappingsurface, the control system 22 may determine that the group ofcomponents is nearing or exceeding its energy usage baseline.

When the energy usage nears or exceeds the energy usage baseline, thecontrol system 22 may determine that the group of components ispotentially faulty (process block 204). As described above, the energyusage baseline describes the expected amount of energy usage.Accordingly, when energy usage nears or exceeds the energy usagebaseline, it is an indication that the energy usage is not as expected.Since the energy usage is not as expected, it may be an indication thatthe group of components is not functioning as expected (e.g.,potentially faulty). Additionally, the control system 22 may notify anoperator of the potentially faulty group of component, for example, bygenerating an alarm that displays an alert on the operator interface 24.

In some embodiments, the control system 22 may identify a part of thecomponent that may be faulty based on the energy usage. For example, ifthe energy usage baseline for a motor drive is 1+/−0.2 kWh, but duringoperation the energy usage is 1.4 kWh, the control system 22 maydetermine that since the energy usage is 70% more than expected a motorbearing in the motor drive is suspected to be faulty. In other words,amount of deviation from the energy usage baseline may be correlatedwith a specific fault that would cause the unexpected energy usage. Insome embodiments, the correlation may be determined based on a previousoperation and/or faults of the component or simulations of such.

Similar to a single component, nearing or exceeding the energy usagebaseline merely indicates that the group of components is potentiallyfaulty. In other words, the group of components may, in fact, notactually be faulty. As such, the control system 22 may notify theoperator of the probability that the component is actually faulty (e.g.,certainty that component is faulty). More specifically, the degree ofcertainty that the component is actually faulty may be based on variousfactors, such as the certainty in the determined energy usage, multipleenergy usage baselines, and the amount of deviation from expected energyusage. For example, when energy usage is 5% outside of the energy usagebaseline, the control system 22 may determine that there is a 60% chancethat a motor drive is faulty and 20% change that the I/O chassis isfaulty. Moreover, determining other operational parameters mayfacilitate identifying which component 20 is actually faulty. Forexample, determining that the current supplied to the motor drive isabove twelve amps may indicate that the motor drive is actually faulty.

In addition to being used to detect when one or more components ispotentially faulty, the energy usage baseline may be used to detectchanges in the operation of one or more components in the industrialautomation system 10. More specifically, as described above, the energyusage baseline may adjust as the control system 22 continues to monitorenergy usage because the determined energy usage values may be used toset a subsequent energy usage baseline. As such, changes in the energyusage baseline may be used to detect changes in operation of a componentor a group of components (e.g., area 16, cell 18, or stage in process).

One embodiment of a process 206 for detecting a change in operation ofone or more components is described in FIG. 15. Generally, the process206 includes determining changes in the energy usage baseline over time(process block 208) and determining changes in operation (process block210). The process 206 may be implemented via machine-readableinstructions stored in the tangible non-transitory memory 36 and/orother memories and executed via processor 34 and/or other processors.

In some embodiments, the control system 22 may keep track of the energyusage baseline to determine the changes in the energy usage baselineover time (process block 208). As described above, the energy usagebaseline may be determined by averaging previous energy usage valuesover a certain period of time. Accordingly, as different (e.g., new)energy usage values are included to determine the energy usage baseline,the energy usage baseline may also change. To facilitate determiningchanges in the energy usage baseline, the processor 36 may storeprevious iterations of the energy usage baseline in memory 38 or otherstorage devices, such as the cloud.

Based on the changes to the energy usage baseline, the control system 22may determine changes have occurred in operation of one or morecomponents (process block 210). More generally, the control system 22may use the previous iterations of the energy usage baseline todetermine trends in energy usage over time. Changes in energy usage overtime may indicate changes in operation of the one or more components.For example, energy usage by a motor drive has increased 3-5% each monthfor the previous three months but is still within its energy usagebaseline. As such, the control system 22 may observe this trend anddetermine that energy usage will exceed the energy usage baseline in twomonths. As such, the control system 22 may recommend replacing the motordrive before the energy usage exceeds the energy usage baseline.

Additionally, changes in energy usage over time may indicate changes inoperating conditions. For example, an increase in energy usage by amixing component may indicate that the consistency of a raw materialprovided by a supplier has changed. Similarly, an increase in energyusage by a component may indicate a change in environmental conditions,such as excessive vibration from a surrounding component. In otherwords, determining changes in operation may enable diagnostics andprognostics, such as scheduling maintenance related events.

To facilitate conveying such information to an operator, the controlsystem 22 or a computing device communicatively coupled to the controlsystem 22 may display a graphical user interface on the operatorinterface 24. One example of a graphical user interface 212 that may bedisplayed by the control system 22 is described in FIG. 16. The depictedgraphical user interface 212 may be used for the packaging factory 50described above. Accordingly, as depicted, the graphical user interface212 includes a loading station graphical element 214, a conveyer sectiongraphical element 216, a washing station graphical element 218, asterilization station graphical element 220, a conveyer sectiongraphical element 222, a filling and sealing station graphical element224, a conveyer section graphical element 226, a labeling stationgraphical element 228, a packing station graphical element 230, and atransport station graphical element 232. The loading station graphicalelement 214 may convey information relating to the loading station 52,the conveyer section graphical element 216 may convey informationrelating to the conveyer section 54, the washing station graphicalelement 218 may convey information relation to the washing station 56,and so on.

In other words, each of the graphical elements (e.g., 216-232) mayindicate the status of the components in the packing factory 50. In someembodiments, the graphical elements may illuminate different colors toindicate the energy usage of each component (e.g., a “heat” map). Forexample, a graphical element may illuminate green when the energy usageis within the energy usage baseline, illuminate yellow when the energyusage nears the energy usage baseline, and illuminate red when theenergy usage exceeds the energy usage baseline. Additionally oralternatively, in other embodiments, the graphical elements mayilluminate different colors to indicate when a maintenance related eventis predicted. For example, a graphical element may illuminate green whenmaintenance is not predicted in the near future, illuminate yellow whenmaintenance is predicted in the near future, and illuminate red whenmaintenance should be performed as soon as possible. Accordingly, bylooking at the color of the graphical elements, an operator may easilydetermine, for example, if a component is potentially faulty or when toperform maintenance on a component.

Additionally, in some embodiments, the information conveyed by thecolors may be changed. For example, at a first time, the control system22 may use colors to indicate energy usage and, at a second time, switchto using the colors to indicate whether a maintenance related activityis predicted. In fact, in some embodiments, text may replace orsupplement the graphical elements. For example, when color is used toindicate energy usage, text may be displayed on each graphical elementto indicate whether maintenance related activity is predicted.

Moreover, in the depicted embodiment, the control system 22 may displaythe graphical elements displayed generally in the same orientation asthe corresponding physical components, which may enable an operator orthe control system 22 to detect (e.g., correlate) conditions that affectmore than one component, such as an area 16 or a cell 18. For example,if the conveyer section graphical element 216 and the washing stationgraphical element 218 both indicate energy usage outside of theirrespective energy usage baselines, an operator or the control system 22may determine that something is affecting energy usage in cell 2, suchas an environmental condition like excessive vibration. In other words,the operator or the control system 22 may determine additionalinformation relating to the packing factory 50 based on the informationindicate by each individual graphical element and the relation of thegraphical elements to one another.

In other embodiments, information relating to energy usage may becommunicated to an operator or the control system 22 through othergraphical user interfaces configurations. More specifically, sinceenergy usage information may determined at various levels ofgranularity, the information may also be communicated to an operator orthe control system 22 with varying levels of granularity. For example, acell 1 graphical element may convey information related to cell 1, acell 2 graphical element may convey information related to cell 2, acell 3 graphical element may convey information related to cell 3, andso on. Similarly, for production process 136, a loading stage graphicalelement may convey information related to the loading stage 138, awashing stage graphical element may convey information related to thewashing stage 140, and a sealing stage graphical element may conveyinformation related to the sealing stage 142. In such embodiments, thegraphical elements may also communicate information by changing colors.

Based on the above described techniques, an energy usage baseline may beused to identify when one or more components 20 are potentially faultyand/or predict when a maintenance related activity should occur. Assuch, technical effects include enabling diagnostics and prognostics oncomponents based on energy usage. More specifically, informationrelating to a component may be determined based on the energy usage inrelation to an energy usage baseline (e.g., expected energy usage). Forexample, an operator may determine a component is potentially faulty ifenergy usage nears or exceeds the energy usage baseline. Additionally,such information may be easily communicated to an operator via agraphical user interface. For example, the graphical user interface mayinclude graphical elements that use color to convey energy usage by acomponent.

Quantifying Energy Performance

As described above, expected energy usage may be determined based onprevious operations and/or models. For example, the expected energyusage of a motor drive may be determined based on how much energy themotor drive was previously used in operation. Additionally, the expectedenergy usage of a motor drive may be determined based on a model of themotor drive, for example, generated based on a manufacturer'sspecifications. More specifically, the expected energy usage may bedetermined based on operations performed by a component or a group ofcomponents. For example, a first expected energy usage may be determinedwhen the motor drive operates at a first speed and a second expectedenergy usage may be determined when the motor drive operates at a secondspeed.

Additionally, the operations performed by a component or group ofcomponents may affect aspects of the industrial automation system 10,such as operating costs or quality of products produced. For example,when the motor drive actuates a motor at a faster speed a product may bemanufactured at a faster rate because the motor may cause a conveyerbelt to turn faster. However, operating at a faster speed may use moreenergy and may affect the quality of the product.

Accordingly, it would be beneficial to include such factors whendetermining an operating strategy for one or more components in anindustrial control system 10. More specifically, as will be described inmore detail below, energy usage may be evaluated along with businessand/or economic considerations to facilitate evaluating productiondecisions, supply chains decisions, make/buy decisions, and the like. Inother words, various sets of operational parameters, such as productionrun rates, production schedule, product recipes, product routing,production methods, supply chain strategies, operating setpoints, andcontrol algorithms, may be analyzed to help an operator or user makesuch decisions. As used herein, each set of operational parameters isdescribed as an “operating strategy.” In other words, design and/oroperation of an industrial automation system 10 may be adjusted (e.g.,by selecting a different operating strategy) based on costs (e.g.,energy usage) and value added (e.g., product quality). As such, anoperator may make an informed decision on how to optimize operation orimprove the efficiency of one or more components in the industrialautomation system 10.

In some embodiments, to facilitate determining executable actions (e.g.,in an operating strategy), factors that may be used to make thedecisions described above may be combined into an economic value-addindex (EVI). For example, as will be described in more detail below, theeconomic value-add index may takes into account costs (e.g., energyusage) as well as value added (e.g., product throughput, productquality, and component up-time). Additionally, in some embodiments,executable actions may be used to dynamically adjustment the operatingstrategies in real-time based on real-time data, such as a sudden changein price per unit of energy usage or newly introduced/detected factors.

One embodiment of a process 234 for determining an operating strategyfor one or more components is described in FIG. 17. Generally, theprocess 234 includes determining multiple operating strategies (processblock 236), determining expected cost for each strategy (process block238), determining value added for each strategy (process block 240), andselecting and executing one of the operating strategies (process block242). The process 234 may be implemented via machine-readableinstructions stored in the tangible non-transitory memory 36 and/orother memories and executed via processor 34 and/or other processors.More specifically, process 234 may be implemented on various levels ofgranularity, for example at a component level, a cell level, an arealevel, a production process level, or a factory level.

In some embodiments, the control system 22 may determine multiplealternative operating strategies that may be implemented in one or morecomponents (process block 236). As described above, various levels ofgranularity may be used. Accordingly, the control system 22 maydetermine operating strategies based on the level of granularity used.For example, when the granularity level is at a component level, thecontrol system 22 may determine various operating strategies for aparticular component. More specifically, the control system 22 maydetermine the various operating strategy in various manners. Forexample, an operator may compare multiple operating strategies byinputting them into the control system 22. Additionally oralternatively, the control system 22 may search for previously or acurrently employed operating strategy by the one or more components.

As described above, each operating strategy may include operationalparameters to be implemented in the industrial automation system 10,such as production run rates, production schedule, product recipes,product routing, operating setpoints, and control algorithms. Forexample, the operating strategy may include a speed at which to drive amotor. Thus, each operating strategy may include different costs as wellas different values added. For example, actuating a motor with a motordrive at a first (e.g., faster) speed may use more energy but enable ahigher production throughput. On the other hand actuating the motor at asecond (e.g., slower) speed may use less energy but have a lowerproduction throughput.

Accordingly, to facilitate selecting an operating strategy to implement,the control system 22 may quantify the expected cost for each operatingstrategy (process block 238). As described above, one cost that may beincluded for each operating strategy is energy usage. To facilitatedetermining the cost associated with energy usage, the control system 22may determine the amount of energy usage expected for each operatingstrategy, for example, using the techniques described above. Forinstance, the control system 22 may predict the energy usage of a motorfor when operating at various speeds using a model of the motorgenerated based on a manufacturer's specifications. Additionally, thecontrol system 22 may determine the price per unit (e.g., kilowatt-hour)of energy usage that a utility provider charges. Thus, the controlsystem 22 may determine cost of the energy usage based on the expectedenergy usage and the price per unit of the energy usage.

In some embodiments, the price per unit of energy usage is not constant.For example, the price may fluctuate based on time of day or day of theweek. Accordingly, the control system 22 may determine factors that mayaffect the price per unit of energy usage. For example, the controlsystem 22 may determine the total energy usage allotment for the one ormore components (process block 244). In some embodiments, a utilityprovider will allot the industrial automation system 10 a specificamount of energy usage. For example, the utility provider may providethe industrial automation system 10 with 500 kWh of energy usage in aday. If the energy usage exceeds the allotment, the utility provider maycharge a premium (e.g., increase the price per unit of energy usage),cutoff energy, or supply energy at a reduced rate. Additionally, in someembodiments, the control system 22 may allot one or more components aspecific amount of energy usage. As such, the control system 22 maydetermine whether the expected energy usage for each operating strategywithin its allotment and the cost of going over the allotment.

Additionally, for example, the control system 22 may determine whetherthe utility provider is expected to charge an energy usage premium(process block 246). In some cases, a utility provider will charge anincrease in price per unit of energy usage to offset its costs. Forexample, when a utility provider's main generator goes offline, theutility provider may have to switch to a backup generator, which mayonly be used a few times a year, to continue providing power to theindustrial automation system 10. However, to offset the maintenancecosts of the backup generator, the utility provider may pass the cost byincreasing price per unit of energy usage while the backup generator isin use. Accordingly, the control system 22 may determine the amount ofthe increased premiums, the expected period of the increased premiums,and the amount of energy usage expected during the period of increasedpremiums. In some embodiments, energy usage premiums may beunpredictable. As such, the control system 22 may determine the costassociated with the energy usage premiums in real-time (e.g., as soon asthe energy usage premium detected).

In addition to the cost associated with energy usage, the control system22 may also determine other costs associated with each operatingstrategy. In some embodiments, the costs associated with an operatingstrategy may include opportunity costs, cost of materials, life cyclecosts, maintenance costs, quality costs, and the like. For example,operating an oven at 100% rated maximum temperature may shorten the lifespan of the oven. Accordingly, the control system 22 may determine thecost associated with the change in the life cycle of the motor driveand/or motor. Additionally, operating a component at a particularoperational parameter may affect the surrounding components. Forexample, actuating a motor at a higher speed may cause additionalvibration that affects the operation of surround components.Accordingly, in order to operate a component at a particular operationalparameter, parts of the industrial automation system 10 may be adjusted.For example, to reduce the effect of the additional vibration, dampersmay be put in place, which increases cost.

The control system 22 may also determine the expected value added foreach operating strategy (process block 240). More specifically, thevalue added may include factors that offset costs, such as improvedthroughput, increased up-time, or increased product quality. In otherwords, the control system 22 may quantify benefits associated with eachoperating strategy. For example, when a motor is actuated at a first(e.g., faster) speed, the throughput may be increased as compared towhen the motor is actuated at a second (e.g., slower) speed.Accordingly, the control system 22 may quantify the value added as aresult of the difference in throughput. Similarly, the control system 22may quantify a difference in product quality and up-time of components.

Based on the expected cost and the value added, the control system 22may select and execute one of the operating strategies (process block242). In some embodiments, the control system 22 may select an operatingstrategy using various optimization techniques. For example, the controlsystem 22 may select the operating strategy that minimizes an objectivefunction of an optimization problem subject to the hard and softconstraints (e.g., energy usage allotment or maximum speed/temperature).In other words, the objective function may be formulated to set criteriafor selecting an operating strategy. For example, in some embodiments,the objective function may be formulated such that the operatingstrategy with the largest difference between the value added and thecosts is selected. In other words, the operating strategy may have apredicted superior economic value. It should be noted that in somesituations the operating strategy selected does not necessarily have theleast amount of energy usage since the energy usage costs may be offsetby an even larger value added.

In other embodiments, the objective function may be formulated to weighcertain criteria (e.g., targets) for each operating strategy. Forexample, the objective function may be formulated to minimize energyusage while maintaining a particular amount of throughput andmaintaining a particular product quality. In other words, the objectivefunction may enable the costs to be minimized while ensuring a certainamount of product quality. As will be described in more detail below, tofacilitate selecting an operating strategy, the cost and value added foreach operating strategy may be quantified into an economic value-addindex (EVI).

As can be appreciated, there may be some amount of uncertainty whendetermining the expected cost and value added for each strategy. Forexample, the uncertainty may result from uncertainty in models used toinfer energy usage, the predictability of energy usage, unpredicted orunexpected operating conditions (e.g., brittle product that is subjectto disturbance), and the like. More specifically, a slight disturbanceto an operating strategy may cause undesired results, such as scrappingan entire batch, which increases cost.

Accordingly, the control system 22 may determine the sensitivity orcertainty of each operating strategy. More specifically, the controlsystem 22 may determine the sensitivity or certainty in various manners,such as based on empirical studies of the materials used, tolerances ofvariations in components, and/or uncertainties in models. The controlsystem 22 may then include the sensitivity or certainty in selecting theoperating strategy. For example, a first operating strategy may have acost to added value difference of 100 units but have a 10% certainty anda second strategy may have a cost to value added difference of 80 unitsbut have an 80% certainty (e.g., more reliable and robust). In such asituation, the control system 22 may select the second operatingstrategy because the effective cost to value added difference of thesecond operating strategy is 64 units (e.g., 80*0.8) as compared to 10units (e.g., 100*0.1) of the first operating strategy.

The control system 22 may execute the selected strategy by implementingthe operating strategy. In some embodiments, the control system 22 mayenable automatically implementing operational parameters included in theselected operating system by transmitting instructions to one or morecomponents. For example, the control system 22 may transmit desiredoperating speed of a motor to a motor drive via the communicationnetwork 29 and, in response, the motor drive may attempt to operate themotor at the desired speed. In other embodiments, the control strategymay notify a user of operational parameters that should be adjusted toimplement the operating strategy. More specifically, this may includeoperational parameters that are not directly related to operation of thecomponents. For example, the control system 22 may inform a user of adesired product mix or raw material quality that should be used via theoperator interface 24.

In addition to implementing an operating strategy based on economicanalysis, an operating strategy may be selected and implemented based onother criteria, such as a carbon footprint. To help illustrate, oneembodiment of a process 248 for determining an operating strategy forone or more components based on carbon footprint is described in FIG.18. Generally, the process 248 includes determining multiple operatingstrategies (process block 250), determining expected carbon costs foreach operating strategy (process block 252), determining value added foreach operating strategy (process block 254), and selecting andimplementing one of the operating strategies (process block 256). Theprocess 234 may be implemented via machine-readable instructions storedin the tangible non-transitory memory 36 and/or other memories andexecuted via processor 34 and/or other processors. More specifically,similar to process 234, process 248 may be implemented on various levelsof granularity, for example at a component level, a cell level, an arealevel, a production process level, or a factory level.

Accordingly, similar to process block 236, the control system 22 maydetermine multiple alternative operating strategies that may beimplemented in one or more components (process block 250). Morespecifically, the control system 22 may determine operating strategiesbased on the level of granularity used, for example at a componentlevel. The control system 22 may determine the various operatingstrategy in various manners, for example, via manual entry or based onpreviously and/or a currently employed operating strategies.

Additionally, each operating strategy may include operational parametersto be implemented in the industrial automation system 10, such asproduction run rates, production schedule, product recipes, productrouting, and energy sourcing. As discussed above, implementing anoperating strategy may use energy, which may be generated via a process(e.g., burning coal) that produces carbon. In some countries, the carbonfootprint is an important consideration, for example, because of thenumber of carbon credits allotted or fees that must be paid based oncarbon footprint.

Accordingly, to facilitate selecting an operating strategy to implement,the control system 22 may quantify the expected carbon cost for eachoperating strategy (process block 252). In some embodiments, the carboncost may be based on the amount of energy used and how the energy wasgenerated. For example, producing 100 kWh of energy by burning coal mayproduce two metric tons of carbon whereas producing 100 kWh of energyusing wind power may produce half a metric ton of carbon. However, theprice per unit of energy usage charged for using the 100 kWh generatedby burning coal may be more cost effective than the 100 kWh generatedusing wind power. As such, the control system 22 may determine theexpected carbon cost by multiplying the expected energy usage with thecarbon produced to generate the energy usage.

Additionally, similar to process block 240, the control system 22 mayalso determine the expected value added for each operating strategy(process block 254). More specifically, the value added may includefactors that offset the carbon costs, such as improved stability of theenergy supply or subsidies provided by a governing body for using energygenerated a particular way (e.g., renewable resources). In other words,the control system 22 may quantify benefits associated with eachoperating strategy. For example, a governing body may give additionalcarbon credits when using energy generated using renewable resources.Accordingly, the control system 22 may quantify the value added as aresult of the additional carbon credits, such as enabling use of thecredits in other parts of the industrial control system or to sell toothers.

Based on the expected carbon cost and the value added, the controlsystem 22 may select and execute one of the operating strategies(process block 256). More specifically, the operating strategy may beselected based on various criteria. For example, in some embodiments, anindustrial control system 10 may be allotted a certain amount of carboncredit. As such, the control system 22 may select operating strategiesthat fit within the carbon credit allotment. More specifically, thecontrol system 22 may determine the number of carbon credits that willbe used in each operating strategy by offsetting the expected carboncosts with the value-added.

Additionally, in some embodiments, the control system 22 may select anoperating strategy that maximizes usage of allotted carbon credits usingvarious optimization techniques. For example, the control system 22 mayselect the operating strategy that minimizes the total carbon footprint.Additionally, the control system 22 may select the operating strategythat uses most or all of the allotted carbon credits.

Similar to the economic analysis described above, selecting an operatingstrategy may additionally include other factors, such as throughput,product quality, up-time, uncertainty/sensitivity, and the like. Forexample, the control system 22 may select the operating strategy thatminimizes carbon footprint while maintaining a minimum level of productthroughput and a minimum level of product quality. Similar to processblock 242, the control system 22 may then execute the selected strategyby implementing the operating strategy in one or more components.

In some instances, carbon credits may have economic value. Morespecifically, unused carbon credits may be sold to others and extracarbon credits may be purchased from others. As such, the carbonfootprint analysis may also be included in the economic analysisdescribed above. In other words, the use of carbon credits above thegiven allotment may be economically quantified based on the cost ofpurchasing more credits from another entity, for example, from agoverning body or another factory. Similarly, unused (e.g., excess)carbon credits may be economically quantified based on the value ofselling the carbon credits to another entity, for example, a governingbody or another factory. In other words, one of ordinary skill in artwill understand that each of the factors (e.g., energy usage, carboncredits, throughput, product quality, and up-time) may be combined invarious analysis (e.g., economic or carbon footprint). Additionally, asdescribed above, each of the factors may be weighted, for examplethrough formulation of an objective function, to select an operatingstrategy with desired characteristics (e.g., a minimum throughput, aminimum product quality, a maximum energy usage, a maximum carbonfootprint, a maximum uncertainty, or a minimum up-time or reliability).

Accordingly, to facilitate taking into account each of the variousfactors, the factors may combined into an economic value-add index(EVI). More specifically, the economic value-add index may quantify theeconomic value added by one unit of operation by a component or a groupof components. One embodiment of an economic value-add index 258 isdepicted in FIG. 19. As depicted, the economic value-add index 258quantifies product throughput 260, product quality 262, componentup-time 264, and energy/carbon usage 262. Generally, the productthroughput 260 describes the amount of product that is output, theproduct quality 262 describes the quality of the output product, and thecomponent up-time describes the reliability of the components. In someembodiments, the factors may be quantified and combined into a singlemetric. For example, each of the factors may be defined economic terms.

To help illustrate, one embodiment of a process 268 for generating theeconomic value-add index for an operating strategy is described in FIG.20. Generally, the process 268 includes determining product throughputof the operating strategy (process block 270), determining productquality of the operating strategy (process block 272), determiningenergy usage or carbon footprint of the operating strategy (processblock 274), determining component up-time (e.g., reliability) of theoperating strategy (process block 276), and generating the economicvalue-add index (process block 278). The process 268 may be implementedvia machine-readable instructions stored in the tangible non-transitorymemory 36 and/or other memories and executed via processor 34 and/orother processors.

In some embodiments, the control system 22 may determine the productthroughout expected if the operating strategy is implemented (processblock 270). More specifically, the product throughput may include thenumber or amount of a product that a component or a group of componentsis expected to output. For example, when a motor actuates a conveyerbelt, the product throughput may include the number of bottles that aretransported on the conveyer belt per minute. Similarly, the productthroughput of a washing stage 140 may include the number of bottles thatare washed by the components in the washing stage 140 per day.

To facilitate determining the expected product throughput, the controlsystem 22 may utilize models to simulate operation of the one or morecomponents that will implement the operating strategy or empiricaltesting. In some embodiments, the model may be based on empiricaltesting and/or manufacturer specifications. For example, as describedabove, the operating strategy may include a specific operating speed forthe motor. As such, the control system 22 may use a model of the motorto simulate operation of the motor at the operating speed and determinethe number of bottles that will be transported per minute. Similarly,the operating strategy may include operational parameters for thecomponents in the washing stage 140. As such, the control system 22 mayuse a model of the washing stage 140 to simulate operation of thecomponents in the washing stage 140 if the operational parameters areimplemented and determine the number of bottles that will be washed perday.

Additionally, the control system 22 may determine the product quality ofthe operating strategy (process block 272). More specifically, theproduct quality may be based on objective or subjective criteria. Insome embodiments, the criteria used is based on the type of product thatis being produced. For example, if the product being produced is washedbottles, the criteria used to determine product quality may includecleanliness of the bottles. Additionally, in some embodiments, theproduct quality may include the effect on overall quality of anintermediate or a final product. For example, the criteria used todetermine product quality for a motor drive may include the effect onconsistency of the bottled beverage produced when operating at thespecific speed.

In some embodiments, the product quality may be determined based onempirical testing. For example, an operator may implement the operatingstrategy and determine product quality by examining the producedproduct. For example, the operator may examine every fifth washed bottleto determine cleanliness of the bottles washed by the washing stage 140.Similarly, the operator may examine every tenth bottle beverage todetermine the effect on consistency resulting from operating the motordrive at different speeds.

Furthermore, the control system 22 may determine the energy usage orcarbon footprint of the operating strategy, for example, using thetechniques described above (process block 274). More specifically, theenergy usage by a component may be inferred based on how much energy themotor drive previously used in operation. Additionally, the expectedenergy usage of a motor drive may be determined based on a model of themotor drive, for example, generated based on a manufacturer'sspecifications or principles of physics. As described above, the carbonfootprint may also be determined in using similar techniques.

The control system 22 may also determine the up-time (e.g., reliability)of the one or more components that will implement the operating strategy(process block 276). More specifically, the up-time of a component maybe based upon the time that is expected between maintenance relatedactivities, such as servicing or replacing a component. As describedabove, operating a component with different operational parameters mayaffect the life span of the component.

In some embodiments, the up-time of one or more components may be basedon energy usage. More specifically, as described above, energy usagethat exceeds an energy usage baseline may indicate the possibility of amaintenance related activity. For example, an operator may run a motorusing the motor drive at an operating speed specified in the operatingstrategy and measure the time until the motor nears or exceeds abaseline to determine the up-time of the motor if the operating strategyis implemented. Moreover, as described above, maintenance related eventsmay also be predicted based on trends in the energy usage. For example,an operator may observe an increasing trend in energy usage by the motordrive and determine that the energy usage will near or exceed the energyusage baseline within two months. As such, the up-time of one or morecomponents (e.g., motor and/or motor drive) may be predicted based ontrends in energy usage.

Based on each of the factors, the control system 22 may combine thefactors into the economic value-add index (process block 278). Asdescribed above, the economic value-add index may combine the factorsinto actionable data. In some embodiments, the economic value-add indexmay be a single metric that can be assigned to an operating strategy. Assuch, an economic value-add index may enable an operator todifferentiate between various operating strategies.

More specifically, the economic value-add index may be generated byquantifying each of the factors into the same metric. For example, theproduct throughput, the product quality, the energy usage/carbonfootprint, and the component up-time may be quantified in economic termsand added together. In some embodiments, economically quantifying eachof the factors may include determining the cost and value added indollars. More specifically, the value added may be positive values andthe cost may be negative values. As such, an operating strategy with ahigher economic value-add index may indicate that it would beadvantageous to implement that operating strategy because implementingthe operating strategy would provide greater economic benefit.

In other embodiments, the economic value-add index may simply be a valuewithin a range. For example, the economic value-add index may be a valuebetween zero and ten with ten being the highest economic benefit andzero being the lowest. As such, the control system 22 may determine theeconomic value-add index by quantifying each of the factors according tothe range. Additionally, in some embodiments, each of the factors may beweighted when generating the economic value-add index. For example, theproduct throughput may be quantified as a value between zero to one, theproduct quality may be a value between zero to two, the componentup-time may be a value between zero to two, and the energy usage may bea value between zero to five. In such an embodiment, the energy usagemay be weighted more heavily than the other factors. In otherembodiments, the factors may be weighted differently, for example, toemphasize product quality. As such, the economic value-add index mayfacilitate determining which operating strategy most fits a particularset of selection criteria, for example, greatest economic benefit (e.g.,difference between value added and cost) or lowest energy usage. Asdescribed above, the control system 22 may then utilize the economicvalue-add index to select an operating strategy to be implemented.

Based on the techniques described above, various operating strategiesmay be compared and/or qualified. Accordingly, technical effects of thepresent disclosure include providing techniques to select and implementan operating strategy from a plurality of operating strategies in anindustrial automation system. Generally, each operating strategy mayaffect operational parameters such as energy usage, product throughput,product quality, or component up-time. Accordingly, to facilitatedifferentiating between various operating strategies, each of thefactors may be quantified. For example, in some embodiments, the factorsmay be quantified as an economic value-add index (EVI), which isassigned to each operating strategy. More specifically, the economicvalue-add index may indicate the economic benefit of an operatingstrategy or how closely the operating strategy fits a particular set ofcriteria. As such, based on the economic value-add index, an operatingstrategy that includes desirable operational parameters will be selectedand implemented in the industrial automation system.

While only certain features of the invention have been illustrated anddescribed herein, many modifications and changes will occur to thoseskilled in the art. It is, therefore, to be understood that the appendedclaims are intended to cover all such modifications and changes as fallwithin the true spirit of the invention.

1. An industrial control system comprising a tangible, non-transitory,computer readable medium storing a plurality of instructions executableby a processor of the industrial control system, the instructionscomprising instructions to: determine a plurality of operatingstrategies associated with an industrial automation component that iscommunicatively coupled to the industrial control system, wherein eachof the plurality of operating strategies comprises a set of operationalparameters associated with the industrial automation component;determine an expected energy usage cost associated with the industrialautomation component for each of the plurality of operating strategies;determine an expected value added associated with the industrialautomation component for each of the plurality of operating strategies;and select one of the plurality of operating strategies for operatingthe industrial automation component based at least in part on theexpected energy usage cost and the expected value added associated witheach of the plurality of operating strategies.
 2. The industrial controlsystem of claim 1, wherein the instructions are configured to operatethe industrial automation component using the selected operatingstrategies by sending one or more operational parameters associated withthe selected operating strategy via a communication network to theindustrial automation component.
 3. The industrial control system ofclaim 1, wherein the instructions are configured to generate a value-addindex comprising the expected energy usage cost and the expected valueadded for each of the plurality of operating strategies, wherein thevalue-add index comprises a product throughput, a product quality, anindustrial component up-time, or any combination thereof, wherein theselected operating strategy comprises a highest value-add index ascompared to other ones of the plurality of operating strategies.
 4. Theindustrial control system of claim 1, wherein the instructions areconfigured to determine a degree of certainty that the expected energyusage cost and the expected value added will be achieved when each ofthe plurality of operating strategies is implemented by the industrialautomation component, wherein the selected operating strategy isselected based at least in part on the degree of certainty of theexpected energy usage cost and the expected value added associated witheach of the plurality of operating strategies.
 5. The industrial controlsystem of claim 1, wherein the instructions to determine the expectedenergy usage comprises instructions to infer the expected energy usageby the industrial automation component based at least in part on energyusage by another related industrial automation component.
 6. Theindustrial control system of claim 1, wherein the instructions areconfigured to determine one or more costs associated with each of theplurality of operating strategies, wherein the selected operatingstrategy is selected based on a difference between the value-added andthe costs associated with each of the plurality of operating strategies.7. The industrial control system of claim 1, wherein the instructionsare configured to receive an energy usage allotment associated with theindustrial automation component, wherein the selected operating strategyis selected based on the energy usage allotment.
 8. The industrialcontrol system of claim 1, wherein the instructions are configured todetect a sudden increase in price per unit of current energy usageassociated with the industrial automation component, wherein theselected operating strategy is selected based at least in part on thesudden increase in price per unit of the current energy usage.
 9. Theindustrial control system of claim 1, wherein each of the plurality ofoperating strategies comprise a production run rate, a productionschedule, a product recipe, a product routing, a production method, asupply chain strategy, a component operating parameter, a controlalgorithm implemented by the industrial control system, or anycombination thereof.
 10. A system, comprising: an industrial automationcomponent; and an industrial control system configured tocommunicatively couple to the industrial automation component, whereinthe industrial control system comprises at least one processorconfigured to: determine a plurality of operating strategies associatedwith the industrial automation component, wherein each of the operatingstrategies comprises a set of operational parameters associated with theindustrial automation component; determine an expected carbon footprintassociated with the industrial automation component for each of theplurality of operating strategies; determine an expected value addedassociated with the industrial automation component for each of theplurality of operating strategies; and select one of the plurality ofoperating strategies for operating the industrial automation componentbased at least in part on the expected energy usage cost and theexpected value added associated with each of the plurality of operatingstrategies.
 11. The system of claim 10, wherein the industrialautomation system is configured to operate the industrial automationcomponent using the selected operating strategy by sending one or moreoperational parameters associated with the selected operating strategyvia a communication network to the industrial automation component. 12.The system of claim 10, wherein the industrial automation system isconfigured to generate a value-add index comprising the expected carbonfootprint and the expected value added for each of the plurality ofoperating strategies, wherein the value added index comprises a productthroughput, a product quality, an industrial component up-time, or anycombination thereof, wherein the selected operating strategy comprises ahighest value-add index compared to the other ones of the plurality ofoperating strategies.
 13. The system of claim 10, wherein the industrialautomation system is configured to determine a degree of certainty thatthe expected carbon footprint and the expected value added will beachieved when each of the plurality of operating strategies isimplemented by the industrial automation component, wherein the selectedoperating strategy is selected based at least in part on the degree ofcertainty of the expected carbon footprint and the expected value addedassociated with each of the plurality of operating strategies.
 14. Thesystem of claim 10, wherein the industrial control system is configuredto determine expected carbon footprint by: determining expected energyusage associated with the industrial automation component for each ofthe plurality of operating strategies; and determining amount of carbonproduced to generate energy used to satisfy the expected energy usage.15. A method comprising: quantifying, using an industrial controlsystem, an expected product throughput for each of a plurality ofoperating strategies configured to be implemented by an industrialautomation component; quantifying, using the industrial control system,an expected product quality for each of the plurality of operatingstrategies; quantifying, using the industrial control system, anexpected energy usage for each of the plurality of operating strategies;quantifying, using the industrial control system, an expected up-timefor each of the plurality of operating strategies; generating, using theindustrial control system, a value-add index based on the expectedproduct throughput, the expected product quality, the expected energyusage, the expected up-time, or any combination thereof; andimplementing, using the industrial control system, one of a plurality ofoperating strategies with the industrial automation component based onthe value-add index.
 16. The method of claim 15, comprising associatingthe expected product throughput with a first weight, the expectedproduct quality with a second weight, the expected energy usage with athird weight, and the expected up-time with a fourth weight based on anobjective function.
 17. The method of claim 15, wherein the expectedproduct throughput, the expected product quality, the expected energyusage, and the expected up-time comprise the same units.
 18. The methodof claim 15, wherein quantifying the expected energy usage comprisesinferring energy usage by the industrial automation component based atleast in part on energy usage by another related industrial automationcomponent.
 19. The method of claim 15, wherein the implemented operatingstrategy comprises a highest value-add index as compared to the otherones of the plurality of operating strategies.
 20. The method of claim15, wherein the value-add index is a single value.